opencv

OpenCV/opencv/doc/CMakeLists.txt

@@ -0,0 +1,315 @@
+if (NOT CMAKE_CROSSCOMPILING)
+  file(RELATIVE_PATH __loc_relative "${OpenCV_BINARY_DIR}" "${CMAKE_CURRENT_LIST_DIR}/pattern_tools\n")
+  file(APPEND "${OpenCV_BINARY_DIR}/opencv_apps_python_tests.cfg" "${__loc_relative}")
+endif()
+
+if(NOT BUILD_DOCS)
+  return()
+endif()
+
+# Dependencies scheme (* - optional):
+#
+# javadoc* -> doxygen_javadoc* -> doxygen_cpp ---------> doxygen -> opencv_docs
+#    \                               \                     /        /
+#     \                               -> doxygen_python* ->        /
+#      ---------------------------------------------------------->
+
+find_package(Doxygen)
+if(DOXYGEN_FOUND)
+  if (DOXYGEN_VERSION VERSION_LESS 1.9.8)
+    message(WARNING "Found doxygen ${DOXYGEN_VERSION}, version 1.9.8 is used for testing, there is "
+                    "a chance your documentation will look different or have some limitations.")
+  endif()
+  add_custom_target(doxygen)
+
+  # not documented modules list
+  set(blacklist "${DOXYGEN_BLACKLIST}")
+  list(APPEND blacklist "ts" "world")
+  unset(CMAKE_DOXYGEN_TUTORIAL_CONTRIB_ROOT)
+  unset(CMAKE_DOXYGEN_TUTORIAL_JS_ROOT)
+
+  set(OPENCV_DOCS_EXCLUDE_CUDA ON)
+  if(";${OPENCV_MODULES_EXTRA};" MATCHES ";cudev;")
+    set(OPENCV_DOCS_EXCLUDE_CUDA OFF)
+    list(APPEND CMAKE_DOXYGEN_ENABLED_SECTIONS "CUDA_MODULES")
+  endif()
+
+  # gathering headers
+  set(paths_include)
+  set(paths_doc)
+  set(paths_bib)
+  set(paths_sample)
+  set(paths_tutorial)
+  set(paths_hal_interface)
+  set(refs_main)
+  set(refs_extra)
+  set(deps)
+  foreach(m ${OPENCV_MODULES_MAIN} ${OPENCV_MODULES_EXTRA})
+    set(the_module "${m}")
+    if(NOT the_module MATCHES "^opencv_")
+      set(the_module "opencv_${m}")
+    endif()
+    list(FIND blacklist ${m} _pos)
+    if(NOT EXISTS "${OPENCV_MODULE_${the_module}_LOCATION}/include"
+        AND NOT EXISTS "${OPENCV_MODULE_${the_module}_LOCATION}/doc"
+    )
+      set(_pos -2)  # blacklist
+    endif()
+    if(${_pos} EQUAL -1)
+      list(APPEND CMAKE_DOXYGEN_ENABLED_SECTIONS "HAVE_opencv_${m}")
+      # include folder
+      set(header_dir "${OPENCV_MODULE_opencv_${m}_LOCATION}/include")
+      if(EXISTS "${header_dir}")
+        list(APPEND paths_include "${header_dir}")
+        list(APPEND deps ${header_dir})
+        if(OPENCV_DOCS_EXCLUDE_CUDA)
+          if(EXISTS "${OPENCV_MODULE_opencv_${m}_LOCATION}/include/opencv2/${m}/cuda")
+            list(APPEND CMAKE_DOXYGEN_EXCLUDE_LIST "${OPENCV_MODULE_opencv_${m}_LOCATION}/include/opencv2/${m}/cuda")
+          endif()
+          file(GLOB list_cuda_files "${OPENCV_MODULE_opencv_${m}_LOCATION}/include/opencv2/${m}/*cuda*.hpp")
+          if(list_cuda_files)
+            list(APPEND CMAKE_DOXYGEN_EXCLUDE_LIST ${list_cuda_files})
+          endif()
+        endif()
+      endif()
+      # doc folder
+      set(docs_dir "${OPENCV_MODULE_opencv_${m}_LOCATION}/doc")
+      if(EXISTS "${docs_dir}")
+        list(APPEND paths_doc "${docs_dir}")
+        list(APPEND deps ${docs_dir})
+      endif()
+      # sample folder
+      set(sample_dir "${OPENCV_MODULE_opencv_${m}_LOCATION}/samples")
+      if(EXISTS "${sample_dir}")
+        list(APPEND paths_sample "${sample_dir}")
+        list(APPEND deps ${sample_dir})
+      endif()
+      # tutorial folder
+      set(tutorial_dir "${OPENCV_MODULE_opencv_${m}_LOCATION}/tutorials")
+      if(EXISTS "${tutorial_dir}")
+        list(APPEND paths_tutorial "${tutorial_dir}")
+        list(APPEND deps ${tutorial_dir})
+
+        # tutorial reference entry
+        file(GLOB tutorials RELATIVE "${OPENCV_MODULE_opencv_${m}_LOCATION}" "${tutorial_dir}/*.markdown")
+        foreach (t ${tutorials})
+          if (NOT DEFINED CMAKE_DOXYGEN_TUTORIAL_CONTRIB_ROOT)
+            set(CMAKE_DOXYGEN_TUTORIAL_CONTRIB_ROOT "- @ref tutorial_contrib_root")
+            set(tutorial_contrib_root "${CMAKE_CURRENT_BINARY_DIR}/contrib_tutorials.markdown")
+            file(WRITE "${tutorial_contrib_root}"
+              "Tutorials for contrib modules {#tutorial_contrib_root}\n"
+              "=============================\n")
+          endif()
+          file(STRINGS "${OPENCV_MODULE_opencv_${m}_LOCATION}/${t}" tutorial_id LIMIT_COUNT 1 REGEX ".*{#[^}]+}")
+          string(REGEX REPLACE ".*{#([^}]+)}" "\\1" tutorial_id "${tutorial_id}")
+          file(APPEND "${tutorial_contrib_root}" "- ${m}. @subpage ${tutorial_id}\n")
+        endforeach()
+      endif()
+      # HAL replacement file
+      set(replacement_header "${OPENCV_MODULE_opencv_${m}_LOCATION}/src/hal_replacement.hpp")
+      if(EXISTS "${replacement_header}")
+        list(APPEND paths_hal_interface "${replacement_header}")
+      endif()
+
+      # BiBTeX file
+      set(bib_file "${docs_dir}/${m}.bib")
+      if(EXISTS "${bib_file}")
+        set(paths_bib "${paths_bib} ${bib_file}")
+        list(APPEND deps ${bib_file})
+      endif()
+      # Reference entry
+      set(one_ref "\t- ${m}. @ref ${m}\n")
+      list(FIND OPENCV_MODULES_EXTRA ${m} _pos)
+      if(${_pos} EQUAL -1)
+        set(refs_main "${refs_main}${one_ref}")
+      else()
+        set(refs_extra "${refs_extra}${one_ref}")
+      endif()
+    endif()
+  endforeach()
+
+  # fix references
+  # set(ref_header "Module name | Folder\n----------- | ------")
+  # if(refs_main)
+  #    set(refs_main "### Main modules\n${ref_header}\n${refs_main}")
+  # endif()
+  # if(refs_extra)
+  #   set(refs_extra "### Extra modules\n${ref_header}\n${refs_extra}")
+  # endif()
+  if(refs_main)
+    set(refs_main "- Main modules:\n${refs_main}")
+  endif()
+  if(refs_extra)
+    set(refs_extra "- Extra modules:\n${refs_extra}")
+  endif()
+
+  # additional config
+  set(doxyfile "${CMAKE_CURRENT_BINARY_DIR}/Doxyfile")
+  set(rootfile "${CMAKE_CURRENT_BINARY_DIR}/root.markdown")
+  set(bibfile "${CMAKE_CURRENT_SOURCE_DIR}/opencv.bib")
+  set(faqfile "${CMAKE_CURRENT_SOURCE_DIR}/faq.markdown")
+  set(tutorial_path "${CMAKE_CURRENT_SOURCE_DIR}/tutorials")
+  set(tutorial_py_path "${CMAKE_CURRENT_SOURCE_DIR}/py_tutorials")
+  set(CMAKE_DOXYGEN_TUTORIAL_JS_ROOT "- @ref tutorial_js_root")
+  set(tutorial_js_path "${CMAKE_CURRENT_SOURCE_DIR}/js_tutorials")
+  set(example_path "${CMAKE_SOURCE_DIR}/samples")
+
+  set(doxygen_image_path
+      ${CMAKE_CURRENT_SOURCE_DIR}/images
+      ${paths_doc}
+      ${tutorial_path}
+      ${tutorial_py_path}
+      ${tutorial_js_path}
+      ${paths_tutorial}
+      #${OpenCV_SOURCE_DIR}/samples/data         # TODO: need to resolve ambiguous conflicts first
+      ${OpenCV_SOURCE_DIR}
+      ${OpenCV_SOURCE_DIR}/modules               # <opencv>/modules
+      ${OPENCV_EXTRA_MODULES_PATH}               # <opencv_contrib>/modules
+      ${OPENCV_DOCS_EXTRA_IMAGE_PATH}            # custom variable for user modules
+  )
+
+  # set export variables
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INPUT_LIST "${rootfile} ; ${faqfile} ; ${paths_include} ; ${paths_hal_interface} ; ${paths_doc} ; ${tutorial_path} ; ${tutorial_py_path} ; ${tutorial_js_path} ; ${paths_tutorial} ; ${tutorial_contrib_root}")
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_IMAGE_PATH "${doxygen_image_path}")
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_EXCLUDE_LIST "${CMAKE_DOXYGEN_EXCLUDE_LIST}")
+  string(REPLACE ";" " " CMAKE_DOXYGEN_ENABLED_SECTIONS "${CMAKE_DOXYGEN_ENABLED_SECTIONS}")
+  # TODO: remove paths_doc from EXAMPLE_PATH after face module tutorials/samples moved to separate folders
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_EXAMPLE_PATH  "${example_path} ; ${paths_doc} ; ${paths_sample}")
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_INCLUDE_ROOTS "${paths_include}")
+  set(CMAKE_DOXYGEN_LAYOUT "${CMAKE_CURRENT_BINARY_DIR}/DoxygenLayout.xml")
+  set(CMAKE_DOXYGEN_OUTPUT_PATH "doxygen")
+  set(CMAKE_DOXYGEN_MAIN_REFERENCE "${refs_main}")
+  set(CMAKE_DOXYGEN_EXTRA_REFERENCE "${refs_extra}")
+  set(CMAKE_EXTRA_BIB_FILES "${bibfile} ${paths_bib}")
+  if (CMAKE_DOXYGEN_GENERATE_QHP)
+    set(CMAKE_DOXYGEN_GENERATE_QHP "YES")
+  else()
+    set(CMAKE_DOXYGEN_GENERATE_QHP "NO")
+  endif()
+
+  list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/opencv.ico")
+  list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/pattern.png")
+  list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/acircles_pattern.png")
+  list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/bodybg.png")
+  # list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/mymath.sty")
+  list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/tutorial-utils.js")
+  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_HTML_FILES "${CMAKE_DOXYGEN_HTML_FILES}")
+
+  if (DOXYGEN_DOT_EXECUTABLE)
+    message(STATUS "Found DOT executable: ${DOXYGEN_DOT_EXECUTABLE}")
+    set(init_dot_path "${DOXYGEN_DOT_EXECUTABLE}")
+    set(init_dot_mode "YES")
+  else()
+    set(init_dot_path "")
+    set(init_dot_mode "NO")
+  endif()
+  set(OPENCV_DOCS_DOT_PATH "${init_dot_path}" CACHE PATH "Doxygen/DOT_PATH value")
+  set(OPENCV_DOCS_HAVE_DOT "${init_dot_mode}" CACHE BOOL "Doxygen: build extra diagrams")
+  set(CMAKECONFIG_DOT_PATH "${OPENCV_DOCS_DOT_PATH}")
+  set(CMAKECONFIG_HAVE_DOT "${OPENCV_DOCS_HAVE_DOT}")
+
+  # 'png' is good enough for compatibility (but requires +50% storage space)
+  set(OPENCV_DOCS_DOT_IMAGE_FORMAT "svg" CACHE STRING "Doxygen/DOT_IMAGE_FORMAT value")
+  set(CMAKECONFIG_DOT_IMAGE_FORMAT "${OPENCV_DOCS_DOT_IMAGE_FORMAT}")
+
+  # Doxygen 1.8.16 fix: https://github.com/doxygen/doxygen/pull/6870
+  # NO is needed here: https://github.com/opencv/opencv/pull/16039
+  set(OPENCV_DOCS_INTERACTIVE_SVG "NO" CACHE BOOL "Doxygen/INTERACTIVE_SVG value")
+  set(CMAKECONFIG_INTERACTIVE_SVG "${OPENCV_DOCS_INTERACTIVE_SVG}")
+
+  set(OPENCV_DOCS_DOXYFILE_IN "Doxyfile.in" CACHE PATH "Doxygen configuration template file (Doxyfile.in)")
+  set(OPENCV_DOCS_DOXYGEN_LAYOUT "DoxygenLayout.xml" CACHE PATH "Doxygen layout file (.xml)")
+
+  # writing file
+  configure_file("${OPENCV_DOCS_DOXYGEN_LAYOUT}" DoxygenLayout.xml @ONLY)
+  configure_file("${OPENCV_DOCS_DOXYFILE_IN}" ${doxyfile} @ONLY)
+  configure_file(root.markdown.in ${rootfile} @ONLY)
+
+  # js tutorial assets
+  set(opencv_tutorial_html_dir "${CMAKE_CURRENT_BINARY_DIR}/doxygen/html")
+  set(js_tutorials_assets_dir "${CMAKE_CURRENT_SOURCE_DIR}/js_tutorials/js_assets")
+  set(js_tutorials_assets_deps "")
+
+  # make sure the build directory exists
+  file(MAKE_DIRECTORY "${opencv_tutorial_html_dir}")
+
+  # gather and copy specific files for js tutorials
+  file(GLOB_RECURSE js_assets "${js_tutorials_assets_dir}/*")
+  ocv_list_filterout(js_assets "\\\\.eslintrc.json")
+  list(APPEND js_assets "${OpenCV_SOURCE_DIR}/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/box.mp4")
+
+  if(BUILD_opencv_js)
+    set(ocv_js_dir "${CMAKE_BINARY_DIR}/bin")
+    set(ocv_js "opencv.js")
+    list(APPEND js_assets "${ocv_js_dir}/${ocv_js}")
+  elseif(DEFINED OPENCV_JS_LOCATION)
+    list(APPEND js_assets "${OPENCV_JS_LOCATION}")
+  endif()
+
+  # copy haar cascade files
+  set(haar_cascade_files "")
+  set(data_harrcascades_path "${OpenCV_SOURCE_DIR}/data/haarcascades/")
+  list(APPEND js_tutorials_assets_deps "${data_harrcascades_path}/haarcascade_frontalface_default.xml" "${data_harrcascades_path}/haarcascade_eye.xml")
+  list(APPEND js_assets "${data_harrcascades_path}/haarcascade_frontalface_default.xml" "${data_harrcascades_path}/haarcascade_eye.xml")
+
+  foreach(f ${js_assets})
+    get_filename_component(fname "${f}" NAME)
+    add_custom_command(OUTPUT "${opencv_tutorial_html_dir}/${fname}"
+                       COMMAND ${CMAKE_COMMAND} -E copy_if_different "${f}" "${opencv_tutorial_html_dir}/${fname}"
+                       DEPENDS "${f}"
+                       COMMENT "Copying ${fname}"
+    )
+    list(APPEND js_tutorials_assets_deps "${f}" "${opencv_tutorial_html_dir}/${fname}")
+  endforeach()
+
+  add_custom_target(
+    doxygen_cpp
+    COMMAND ${DOXYGEN_EXECUTABLE} ${doxyfile}
+    DEPENDS ${doxyfile} ${rootfile} ${bibfile} ${deps} ${js_tutorials_assets_deps}
+    COMMENT "Generate Doxygen documentation"
+  )
+
+  if(NOT DEFINED HAVE_PYTHON_BS4 AND PYTHON_DEFAULT_EXECUTABLE)
+    # Documentation post-processing tool requires BuautifulSoup Python package
+    execute_process(COMMAND "${PYTHON_DEFAULT_EXECUTABLE}" -c "import bs4; from bs4 import BeautifulSoup; print(bs4.__version__)"
+                    RESULT_VARIABLE _result
+                    OUTPUT_VARIABLE _bs4_version
+                    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(NOT _result EQUAL 0)
+      set(HAVE_PYTHON_BS4 0 CACHE INTERNAL "")
+    else()
+      message(STATUS "Python BeautifulSoup (bs4) version: ${_bs4_version}")
+      set(HAVE_PYTHON_BS4 1 CACHE INTERNAL "")
+    endif()
+  endif()
+
+  if(PYTHON_DEFAULT_EXECUTABLE
+      AND HAVE_PYTHON_BS4
+      AND OPENCV_PYTHON_SIGNATURES_FILE
+      AND TARGET gen_opencv_python_source)
+    add_custom_target(doxygen_python
+      COMMAND ${PYTHON_DEFAULT_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/tools/add_signatures.py" "${CMAKE_CURRENT_BINARY_DIR}/doxygen/html/" "${OPENCV_PYTHON_SIGNATURES_FILE}" "python"
+      DEPENDS doxygen_cpp gen_opencv_python_source
+      COMMENT "Inject Python signatures into documentation"
+    )
+  endif()
+
+  add_dependencies(doxygen doxygen_cpp)
+
+  if(TARGET doxygen_python)
+    add_dependencies(doxygen doxygen_python)
+  endif()
+
+  if(TARGET doxygen_javadoc)
+    add_dependencies(doxygen_cpp doxygen_javadoc)
+  endif()
+
+  add_dependencies(opencv_docs doxygen)
+
+  install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/doxygen/html
+    DESTINATION "${OPENCV_DOC_INSTALL_PATH}"
+    COMPONENT "docs" OPTIONAL
+    ${compatible_MESSAGE_NEVER}
+  )
+endif()

OpenCV/opencv/doc/Doxyfile.in

@@ -0,0 +1,351 @@
+DOXYFILE_ENCODING      = UTF-8
+PROJECT_NAME           = OpenCV
+PROJECT_NUMBER         = @OPENCV_VERSION@
+PROJECT_BRIEF          = "Open Source Computer Vision"
+PROJECT_LOGO           = @CMAKE_CURRENT_SOURCE_DIR@/opencv-logo-small.png
+#PROJECT_ICON           =
+OUTPUT_DIRECTORY       = @CMAKE_DOXYGEN_OUTPUT_PATH@
+CREATE_SUBDIRS         = YES
+CREATE_SUBDIRS_LEVEL   = 8
+ALLOW_UNICODE_NAMES    = NO
+OUTPUT_LANGUAGE        = English
+BRIEF_MEMBER_DESC      = YES
+REPEAT_BRIEF           = YES
+ABBREVIATE_BRIEF       = "The $name class" \
+                         "The $name widget" \
+                         "The $name file" \
+                         is \
+                         provides \
+                         specifies \
+                         contains \
+                         represents \
+                         a \
+                         an \
+                         the
+ALWAYS_DETAILED_SEC    = NO
+INLINE_INHERITED_MEMB  = NO
+FULL_PATH_NAMES        = YES
+STRIP_FROM_PATH        = @CMAKE_SOURCE_DIR@/modules @CMAKE_DOXYGEN_INCLUDE_ROOTS@
+STRIP_FROM_INC_PATH    = @CMAKE_DOXYGEN_INCLUDE_ROOTS@
+SHORT_NAMES            = NO
+JAVADOC_AUTOBRIEF      = NO
+JAVADOC_BANNER         = NO
+QT_AUTOBRIEF           = NO
+MULTILINE_CPP_IS_BRIEF = NO
+PYTHON_DOCSTRING       = YES
+INHERIT_DOCS           = YES
+SEPARATE_MEMBER_PAGES  = NO
+TAB_SIZE               = 4
+ALIASES               += add_toggle{1}="@htmlonly[block] <div class='newInnerHTML'>\1</div><div> <script type='text/javascript'> addToggle(); </script>@endhtmlonly"
+ALIASES               += add_toggle_cpp="@htmlonly[block] <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'>@endhtmlonly"
+ALIASES               += add_toggle_java="@htmlonly[block] <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'>@endhtmlonly"
+ALIASES               += add_toggle_python="@htmlonly[block] <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'>@endhtmlonly"
+ALIASES               += end_toggle="@htmlonly[block] </div> @endhtmlonly"
+ALIASES               += prev_tutorial{1}="**Prev  Tutorial:** \ref \1 \n"
+ALIASES               += next_tutorial{1}="**Next  Tutorial:** \ref \1 \n"
+ALIASES               += youtube{1}="@htmlonly[block]<div align='center'><iframe title='Video' width='560' height='349' src='https://www.youtube.com/embed/\1?rel=0' frameborder='0' align='middle' allowfullscreen></iframe></div>@endhtmlonly"
+OPTIMIZE_OUTPUT_FOR_C  = NO
+OPTIMIZE_OUTPUT_JAVA   = NO
+OPTIMIZE_FOR_FORTRAN   = NO
+OPTIMIZE_OUTPUT_VHDL   = NO
+OPTIMIZE_OUTPUT_SLICE  = NO
+EXTENSION_MAPPING      =
+MARKDOWN_SUPPORT       = YES
+TOC_INCLUDE_HEADINGS   = 5
+MARKDOWN_ID_STYLE      = DOXYGEN
+AUTOLINK_SUPPORT       = YES
+BUILTIN_STL_SUPPORT    = YES
+CPP_CLI_SUPPORT        = NO
+SIP_SUPPORT            = NO
+IDL_PROPERTY_SUPPORT   = YES
+DISTRIBUTE_GROUP_DOC   = NO
+GROUP_NESTED_COMPOUNDS = NO
+SUBGROUPING            = YES
+INLINE_GROUPED_CLASSES = NO
+INLINE_SIMPLE_STRUCTS  = NO
+TYPEDEF_HIDES_STRUCT   = YES
+LOOKUP_CACHE_SIZE      = 0
+NUM_PROC_THREADS       = 1
+TIMESTAMP              = YES
+EXTRACT_ALL            = YES
+EXTRACT_PRIVATE        = NO
+EXTRACT_PRIV_VIRTUAL   = NO
+EXTRACT_PACKAGE        = NO
+EXTRACT_STATIC         = YES
+EXTRACT_LOCAL_CLASSES  = NO
+EXTRACT_LOCAL_METHODS  = NO
+EXTRACT_ANON_NSPACES   = NO
+RESOLVE_UNNAMED_PARAMS = YES
+HIDE_UNDOC_MEMBERS     = NO
+HIDE_UNDOC_CLASSES     = NO
+HIDE_FRIEND_COMPOUNDS  = NO
+HIDE_IN_BODY_DOCS      = NO
+INTERNAL_DOCS          = NO
+CASE_SENSE_NAMES       = YES
+HIDE_SCOPE_NAMES       = NO
+HIDE_COMPOUND_REFERENCE= NO
+SHOW_HEADERFILE        = YES
+SHOW_INCLUDE_FILES     = YES
+SHOW_GROUPED_MEMB_INC  = YES
+FORCE_LOCAL_INCLUDES   = NO
+INLINE_INFO            = YES
+SORT_MEMBER_DOCS       = YES
+SORT_BRIEF_DOCS        = YES
+SORT_MEMBERS_CTORS_1ST = YES
+SORT_GROUP_NAMES       = NO
+SORT_BY_SCOPE_NAME     = NO
+STRICT_PROTO_MATCHING  = NO
+GENERATE_TODOLIST      = YES
+GENERATE_TESTLIST      = YES
+GENERATE_BUGLIST       = YES
+GENERATE_DEPRECATEDLIST= YES
+ENABLED_SECTIONS       = @CMAKE_DOXYGEN_ENABLED_SECTIONS@
+MAX_INITIALIZER_LINES  = 30
+SHOW_USED_FILES        = YES
+SHOW_FILES             = YES
+SHOW_NAMESPACES        = YES
+FILE_VERSION_FILTER    =
+LAYOUT_FILE            = @CMAKE_DOXYGEN_LAYOUT@
+CITE_BIB_FILES         = @CMAKE_EXTRA_BIB_FILES@
+QUIET                  = YES
+WARNINGS               = YES
+WARN_IF_UNDOCUMENTED   = YES
+WARN_IF_DOC_ERROR      = YES
+WARN_IF_INCOMPLETE_DOC = YES
+WARN_NO_PARAMDOC       = NO
+WARN_IF_UNDOC_ENUM_VAL = NO
+WARN_AS_ERROR          = NO
+WARN_FORMAT            = "$file:$line: $text"
+WARN_LINE_FORMAT       = "at line $line of file $file"
+WARN_LOGFILE           =
+INPUT                  = @CMAKE_DOXYGEN_INPUT_LIST@
+INPUT_ENCODING         = UTF-8
+INPUT_FILE_ENCODING    =
+FILE_PATTERNS          =
+RECURSIVE              = YES
+EXCLUDE                = @CMAKE_DOXYGEN_EXCLUDE_LIST@
+EXCLUDE_SYMLINKS       = NO
+EXCLUDE_PATTERNS       = *.inl.hpp *.impl.hpp *_detail.hpp */cudev/**/detail/*.hpp *.m */opencl/runtime/* */legacy/* *_c.h @DOXYGEN_EXCLUDE_PATTERNS@
+EXCLUDE_SYMBOLS        = cv::DataType<*> cv::traits::* int void CV__* T __CV* cv::gapi::detail*
+EXAMPLE_PATH           = @CMAKE_DOXYGEN_EXAMPLE_PATH@
+EXAMPLE_PATTERNS       = *
+EXAMPLE_RECURSIVE      = YES
+IMAGE_PATH             = @CMAKE_DOXYGEN_IMAGE_PATH@
+INPUT_FILTER           =
+FILTER_PATTERNS        =
+FILTER_SOURCE_FILES    = NO
+FILTER_SOURCE_PATTERNS =
+USE_MDFILE_AS_MAINPAGE =
+SOURCE_BROWSER         = NO
+INLINE_SOURCES         = NO
+STRIP_CODE_COMMENTS    = YES
+REFERENCED_BY_RELATION = NO
+REFERENCES_RELATION    = NO
+REFERENCES_LINK_SOURCE = YES
+SOURCE_TOOLTIPS        = YES
+USE_HTAGS              = NO
+VERBATIM_HEADERS       = NO
+CLANG_ASSISTED_PARSING = NO
+CLANG_ADD_INC_PATHS    = YES
+CLANG_OPTIONS          =
+CLANG_DATABASE_PATH    =
+ALPHABETICAL_INDEX     = YES
+IGNORE_PREFIX          =
+GENERATE_HTML          = YES
+HTML_OUTPUT            = html
+HTML_FILE_EXTENSION    = .html
+HTML_HEADER            = @CMAKE_CURRENT_SOURCE_DIR@/header.html
+HTML_FOOTER            = @CMAKE_CURRENT_SOURCE_DIR@/footer.html
+HTML_STYLESHEET        =
+HTML_EXTRA_STYLESHEET  = @CMAKE_CURRENT_SOURCE_DIR@/stylesheet.css
+HTML_EXTRA_FILES       = @CMAKE_DOXYGEN_HTML_FILES@
+HTML_COLORSTYLE        = LIGHT
+HTML_COLORSTYLE_HUE    = 220
+HTML_COLORSTYLE_SAT    = 100
+HTML_COLORSTYLE_GAMMA  = 80
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OpenCV/opencv/doc/DoxygenLayout.xml

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OpenCV/opencv/doc/LICENSE_BSD.txt

@@ -0,0 +1,42 @@
+By downloading, copying, installing or using the software you agree to this license.
+If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2000-2020, Intel Corporation, all rights reserved.
+Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+Copyright (C) 2009-2016, NVIDIA Corporation, all rights reserved.
+Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+Copyright (C) 2015-2016, OpenCV Foundation, all rights reserved.
+Copyright (C) 2015-2016, Itseez Inc., all rights reserved.
+Copyright (C) 2019-2020, Xperience AI, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are disclaimed.
+In no event shall copyright holders or contributors be liable for any direct,
+indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.

OpenCV/opencv/doc/LICENSE_CHANGE_NOTICE.txt

@@ -0,0 +1,47 @@
+Starting from OpenCV 4.5-pre (2020 August) OpenCV has changed the license from BSD to Apache 2. See https://opencv.org/blog/opencv-is-to-change-the-license-to-apache-2/ and https://github.com/opencv/opencv/wiki/OE-32.--Change-OpenCV-License-to-Apache-2 for details.
+
+Here is the original OpenCV license:
+------------------------------------------------------------------------------------
+By downloading, copying, installing or using the software you agree to this license.
+If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2000-2020, Intel Corporation, all rights reserved.
+Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+Copyright (C) 2009-2016, NVIDIA Corporation, all rights reserved.
+Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+Copyright (C) 2015-2016, OpenCV Foundation, all rights reserved.
+Copyright (C) 2015-2016, Itseez Inc., all rights reserved.
+Copyright (C) 2019-2020, Xperience AI, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are disclaimed.
+In no event shall copyright holders or contributors be liable for any direct,
+indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+------------------------------------------------------------------------------------

OpenCV/opencv/doc/disabled_doc_warnings.txt

@@ -0,0 +1,2 @@
+# doxygen citelist build workaround
+citelist : .*Unexpected new line character.*

OpenCV/opencv/doc/faq.markdown

@@ -0,0 +1,4 @@
+Frequently Asked Questions {#faq}
+==========================
+
+Compatibility page. FAQ migrated to the project [wiki](https://github.com/opencv/opencv/wiki/FAQ).

OpenCV/opencv/doc/footer.html

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+    <img class="footer" src="$relpath^doxygen.png" alt="doxygen"/></a> $doxygenversion </li>
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OpenCV/opencv/doc/header.html

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+<meta name="generator" content="Doxygen $doxygenversion"/>
+<!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME-->
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OpenCV/opencv/doc/js_tutorials/js_assets/.eslintrc.json

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+  "extends": "google",
+  "parserOptions": {
+    "ecmaVersion": 6
+  },
+  "rules": {
+    "max-len": ["error", 100, {"ignoreUrls": true}],
+    "quotes": ["error", "single"],
+    "indent": ["error", 4, {"ArrayExpression": "first",
+                            "ObjectExpression": "first",
+                            "CallExpression": {"arguments": "first"},
+                            "SwitchCase": 1}],
+    "require-jsdoc": "off",
+    "new-cap": "off"
+  },
+  "plugins": ["html"],
+  "settings": {
+    "html/javascript-mime-types": ["text/javascript", "text/code-snippet"],
+    "html/indent": "0",
+    "html/report-bad-indent": "error"
+  }
+}

OpenCV/opencv/doc/js_tutorials/js_assets/js_basic_ops_copymakeborder.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Padding Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Padding Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// You can try more different parameters
+let s = new cv.Scalar(255, 0, 0, 255);
+cv.copyMakeBorder(src, dst, 10, 10, 10, 10, cv.BORDER_CONSTANT, s);
+cv.imshow('canvasOutput', dst);
+src.delete();
+dst.delete();
+</script>
+<script>
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_basic_ops_roi.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image ROI Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image ROI Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// You can try more different parameters
+let rect = new cv.Rect(100, 100, 200, 200);
+dst = src.roi(rect);
+cv.imshow('canvasOutput', dst);
+src.delete();
+dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_bg_subtraction.html

@@ -0,0 +1,126 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Background Subtraction Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Background Subtraction Example</h2>
+<p>
+    Click <b>Start/Stop</b> button to start or stop the camera capture.<br>
+    The <b>videoInput</b> is a &lt;video&gt; element used as input.
+    The <b>canvasOutput</b> is a &lt;canvas&gt; element used as output.<br>
+    The code of &lt;textarea&gt; will be executed when video is started.
+    You can modify the code to investigate more.
+</p>
+<div>
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<textarea class="code" rows="29" cols="80" id="codeEditor" spellcheck="false">
+</textarea>
+</div>
+<p class="err" id="errorMessage"></p>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <video id="videoInput" width="320" height="240" muted loop></video>
+        </td>
+        <td>
+            <canvas id="canvasOutput" width="320" height="240"></canvas>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">videoInput</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    </table>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let video = document.getElementById('videoInput');
+let cap = new cv.VideoCapture(video);
+
+let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let fgmask = new cv.Mat(video.height, video.width, cv.CV_8UC1);
+let fgbg = new cv.BackgroundSubtractorMOG2(500, 16, true);
+
+const FPS = 30;
+function processVideo() {
+    try {
+        if (!streaming) {
+            // clean and stop.
+            frame.delete(); fgmask.delete(); fgbg.delete();
+            return;
+        }
+        let begin = Date.now();
+        // start processing.
+        cap.read(frame);
+        fgbg.apply(frame, fgmask);
+        cv.imshow('canvasOutput', fgmask);
+        // schedule the next one.
+        let delay = 1000/FPS - (Date.now() - begin);
+        setTimeout(processVideo, delay);
+    } catch (err) {
+        utils.printError(err);
+    }
+};
+
+// schedule the first one.
+setTimeout(processVideo, 0);
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+
+let streaming = false;
+let videoInput = document.getElementById('videoInput');
+let startAndStop = document.getElementById('startAndStop');
+let canvasOutput = document.getElementById('canvasOutput');
+let canvasContext = canvasOutput.getContext('2d');
+
+startAndStop.addEventListener('click', () => {
+    if (!streaming) {
+        utils.clearError();
+        videoInput.play().then(() => {
+            onVideoStarted();
+        });
+    } else {
+        videoInput.pause();
+        videoInput.currentTime = 0;
+        onVideoStopped();
+    }
+});
+
+function onVideoStarted() {
+    streaming = true;
+    startAndStop.innerText = 'Stop';
+    videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
+    utils.executeCode('codeEditor');
+}
+
+function onVideoStopped() {
+    streaming = false;
+    canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
+    startAndStop.innerText = 'Start';
+}
+
+utils.loadOpenCv(() => {
+    videoInput.addEventListener('canplay', () => {
+        startAndStop.removeAttribute('disabled');
+    });
+    videoInput.src = 'box.mp4';
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_camshift.html

@@ -0,0 +1,172 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>CamShift Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>CamShift Example</h2>
+<p>
+    Click <b>Start/Stop</b> button to start or stop the video.<br>
+    The <b>videoInput</b> is a &lt;video&gt; element used as CamShift input.
+    The <b>canvasOutput</b> is a &lt;canvas&gt; element used as CamShift output.<br>
+    The code of &lt;textarea&gt; will be executed when video is started.
+    You can modify the code to investigate more.
+</p>
+<div>
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<textarea class="code" rows="29" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+</div>
+<p class="err" id="errorMessage"></p>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <video id="videoInput" width="320" height="240" muted loop></video>
+        </td>
+        <td>
+            <canvas id="canvasOutput" width="320" height="240"></canvas>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">videoInput</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    </table>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let video = document.getElementById('videoInput');
+let cap = new cv.VideoCapture(video);
+
+// take first frame of the video
+let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+cap.read(frame);
+
+// hardcode the initial location of window
+let trackWindow = new cv.Rect(150, 60, 63, 125);
+
+// set up the ROI for tracking
+let roi = frame.roi(trackWindow);
+let hsvRoi = new cv.Mat();
+cv.cvtColor(roi, hsvRoi, cv.COLOR_RGBA2RGB);
+cv.cvtColor(hsvRoi, hsvRoi, cv.COLOR_RGB2HSV);
+let mask = new cv.Mat();
+let lowScalar = new cv.Scalar(30, 30, 0);
+let highScalar = new cv.Scalar(180, 180, 180);
+let low = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), lowScalar);
+let high = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), highScalar);
+cv.inRange(hsvRoi, low, high, mask);
+let roiHist = new cv.Mat();
+let hsvRoiVec = new cv.MatVector();
+hsvRoiVec.push_back(hsvRoi);
+cv.calcHist(hsvRoiVec, [0], mask, roiHist, [180], [0, 180]);
+cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX);
+
+// delete useless mats.
+roi.delete(); hsvRoi.delete(); mask.delete(); low.delete(); high.delete(); hsvRoiVec.delete();
+
+// Setup the termination criteria, either 10 iteration or move by at least 1 pt
+let termCrit = new cv.TermCriteria(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1);
+
+let hsv = new cv.Mat(video.height, video.width, cv.CV_8UC3);
+let hsvVec = new cv.MatVector();
+hsvVec.push_back(hsv);
+let dst = new cv.Mat();
+let trackBox = null;
+
+const FPS = 30;
+function processVideo() {
+    try {
+        if (!streaming) {
+            // clean and stop.
+            frame.delete(); dst.delete(); hsvVec.delete(); roiHist.delete(); hsv.delete();
+            return;
+        }
+        let begin = Date.now();
+
+        // start processing.
+        cap.read(frame);
+        cv.cvtColor(frame, hsv, cv.COLOR_RGBA2RGB);
+        cv.cvtColor(hsv, hsv, cv.COLOR_RGB2HSV);
+        cv.calcBackProject(hsvVec, [0], roiHist, dst, [0, 180], 1);
+
+        // apply camshift to get the new location
+        [trackBox, trackWindow] = cv.CamShift(dst, trackWindow, termCrit);
+
+        // Draw it on image
+        let pts = cv.rotatedRectPoints(trackBox);
+        cv.line(frame, pts[0], pts[1], [255, 0, 0, 255], 3);
+        cv.line(frame, pts[1], pts[2], [255, 0, 0, 255], 3);
+        cv.line(frame, pts[2], pts[3], [255, 0, 0, 255], 3);
+        cv.line(frame, pts[3], pts[0], [255, 0, 0, 255], 3);
+        cv.imshow('canvasOutput', frame);
+
+        // schedule the next one.
+        let delay = 1000/FPS - (Date.now() - begin);
+        setTimeout(processVideo, delay);
+    } catch (err) {
+        utils.printError(err);
+    }
+};
+
+// schedule the first one.
+setTimeout(processVideo, 0);
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+
+let streaming = false;
+let videoInput = document.getElementById('videoInput');
+let startAndStop = document.getElementById('startAndStop');
+let canvasOutput = document.getElementById('canvasOutput');
+let canvasContext = canvasOutput.getContext('2d');
+
+startAndStop.addEventListener('click', () => {
+    if (!streaming) {
+        utils.clearError();
+        videoInput.play().then(() => {
+            onVideoStarted();
+        });
+    } else {
+        videoInput.pause();
+        videoInput.currentTime = 0;
+        onVideoStopped();
+    }
+});
+
+function onVideoStarted() {
+    streaming = true;
+    startAndStop.innerText = 'Stop';
+    videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
+    utils.executeCode('codeEditor');
+}
+
+function onVideoStopped() {
+    streaming = false;
+    canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
+    startAndStop.innerText = 'Start';
+}
+
+utils.loadOpenCv(() => {
+    videoInput.addEventListener('canplay', () => {
+        startAndStop.removeAttribute('disabled');
+    });
+    videoInput.src = 'cup.mp4';
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_canny.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Canny Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Canny Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGB2GRAY, 0);
+// You can try more different parameters
+cv.Canny(src, dst, 50, 100, 3, false);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_colorspaces_cvtColor.html

@@ -0,0 +1,67 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Convert Color Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Convert Color Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// You can try more different parameters
+cv.cvtColor(src, dst, cv.COLOR_RGBA2GRAY, 0);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_colorspaces_inRange.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image InRange Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image InRange Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let low = new cv.Mat(src.rows, src.cols, src.type(), [0, 0, 0, 0]);
+let high = new cv.Mat(src.rows, src.cols, src.type(), [150, 150, 150, 255]);
+// You can try more different parameters
+cv.inRange(src, low, high, dst);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); low.delete(); high.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_approxPolyDP.html

@@ -0,0 +1,86 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image ApproxPolyDP Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image ApproxPolyDP Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 100, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+let poly = new cv.MatVector();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+// approximates each contour to polygon
+for (let i = 0; i < contours.size(); ++i) {
+    let tmp = new cv.Mat();
+    let cnt = contours.get(i);
+    // You can try more different parameters
+    cv.approxPolyDP(cnt, tmp, 3, true);
+    poly.push_back(tmp);
+    cnt.delete(); tmp.delete();
+}
+// draw contours with random Scalar
+for (let i = 0; i < contours.size(); ++i) {
+    let color = new cv.Scalar(Math.round(Math.random() * 255), Math.round(Math.random() * 255),
+                              Math.round(Math.random() * 255));
+    cv.drawContours(dst, poly, i, color, 1, 8, hierarchy, 0);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); hierarchy.delete(); contours.delete(); poly.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_area.html

@@ -0,0 +1,63 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Area Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Area Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <div>
+        <canvas id="canvasInput"></canvas>
+        <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+    </div>
+    <div>
+        <p><strong>The area is: </strong><span id="areaOutput"></span></p>
+    </div>
+
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(20);
+// You can try more different parameters
+let area = cv.contourArea(cnt, false);
+areaOutput.innerHTML = area;
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_boundingRect.html

@@ -0,0 +1,79 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Bounding Rect Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Bounding Rect Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(0);
+// You can try more different parameters
+let rect = cv.boundingRect(cnt);
+let contoursColor = new cv.Scalar(255, 255, 255);
+let rectangleColor = new cv.Scalar(255, 0, 0);
+cv.drawContours(dst, contours, 0, contoursColor, 1, 8, hierarchy, 100);
+let point1 = new cv.Point(rect.x, rect.y);
+let point2 = new cv.Point(rect.x + rect.width, rect.y + rect.height);
+cv.rectangle(dst, point1, point2, rectangleColor, 2, cv.LINE_AA, 0);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_convexHull.html

@@ -0,0 +1,86 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Convex Hull Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Convex Hull Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 100, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+let hull = new cv.MatVector();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+// approximates each contour to convex hull
+for (let i = 0; i < contours.size(); ++i) {
+    let tmp = new cv.Mat();
+    let cnt = contours.get(i);
+    // You can try more different parameters
+    cv.convexHull(cnt, tmp, false, true);
+    hull.push_back(tmp);
+    cnt.delete(); tmp.delete();
+}
+// draw contours with random Scalar
+for (let i = 0; i < contours.size(); ++i) {
+    let colorHull = new cv.Scalar(Math.round(Math.random() * 255), Math.round(Math.random() * 255),
+                                  Math.round(Math.random() * 255));
+    cv.drawContours(dst, hull, i, colorHull, 1, 8, hierarchy, 0);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); hierarchy.delete(); contours.delete(); hull.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_fitEllipse.html

@@ -0,0 +1,77 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Fit Ellipse Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Fit Ellipse Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(0);
+// You can try more different parameters
+let rotatedRect = cv.fitEllipse(cnt);
+let contoursColor = new cv.Scalar(255, 255, 255);
+let ellipseColor = new cv.Scalar(255, 0, 0);
+cv.drawContours(dst, contours, 0, contoursColor, 1, 8, hierarchy, 100);
+cv.ellipse1(dst, rotatedRect, ellipseColor, 1, cv.LINE_8);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_fitLine.html

@@ -0,0 +1,86 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Fit Line Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Fit Line Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+let line = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(0);
+// You can try more different parameters
+cv.fitLine(cnt, line, cv.DIST_L2, 0, 0.01, 0.01);
+let contoursColor = new cv.Scalar(255, 255, 255);
+let lineColor = new cv.Scalar(255, 0, 0);
+cv.drawContours(dst, contours, 0, contoursColor, 1, 8, hierarchy, 100);
+let vx = line.data32F[0];
+let vy = line.data32F[1];
+let x = line.data32F[2];
+let y = line.data32F[3];
+let lefty = Math.round((-x * vy / vx) + y);
+let righty = Math.round(((src.cols - x) * vy / vx) + y);
+let point1 = new cv.Point(src.cols - 1, righty);
+let point2 = new cv.Point(0, lefty);
+cv.line(dst, point1, point2, lineColor, 2, cv.LINE_AA, 0);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); line.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_minAreaRect.html

@@ -0,0 +1,81 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Min Area Rect Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Min Area Rect Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(0);
+// You can try more different parameters
+let rotatedRect = cv.minAreaRect(cnt);
+let vertices = cv.RotatedRect.points(rotatedRect);
+let contoursColor = new cv.Scalar(255, 255, 255);
+let rectangleColor = new cv.Scalar(255, 0, 0);
+cv.drawContours(dst, contours, 0, contoursColor, 1, 8, hierarchy, 100);
+// draw rotatedRect
+for (let i = 0; i < 4; i++) {
+    cv.line(dst, vertices[i], vertices[(i + 1) % 4], rectangleColor, 2, cv.LINE_AA, 0);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_minEnclosingCircle.html

@@ -0,0 +1,77 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Min Enclosing Circle Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Min Enclosing Circle Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(0);
+// You can try more different parameters
+let circle = cv.minEnclosingCircle(cnt);
+let contoursColor = new cv.Scalar(255, 255, 255);
+let circleColor = new cv.Scalar(255, 0, 0);
+cv.drawContours(dst, contours, 0, contoursColor, 1, 8, hierarchy, 100);
+cv.circle(dst, circle.center, circle.radius, circleColor);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_moments.html

@@ -0,0 +1,62 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Moments Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Moments Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <div>
+        <canvas id="canvasInput"></canvas>
+        <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+    </div>
+    <div>
+        <p><strong>The m00 is: </strong><span id="momentsOutput"></span></p>
+    </div>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(0);
+// You can try more different parameters
+let Moments = cv.moments(cnt, false);
+momentsOutput.innerHTML = Moments.m00;
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_features_perimeter.html

@@ -0,0 +1,62 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Perimeter Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Perimeter Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <div>
+        <canvas id="canvasInput"></canvas>
+        <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+    </div>
+    <div>
+        <p><strong>The perimeter is: </strong><span id="perimeterOutput"></span></p>
+    </div>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let cnt = contours.get(20);
+// You can try more different parameters
+let perimeter = cv.arcLength(cnt, true);
+perimeterOutput.innerHTML = perimeter;
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete(); cnt.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contour_properties_transpose.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Transpose Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Transpose Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 120, 200, cv.THRESH_BINARY);
+cv.transpose(src, dst);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contours_begin_contours.html

@@ -0,0 +1,77 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Contours Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Contours Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 120, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+// You can try more different parameters
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+// draw contours with random Scalar
+for (let i = 0; i < contours.size(); ++i) {
+    let color = new cv.Scalar(Math.round(Math.random() * 255), Math.round(Math.random() * 255),
+                              Math.round(Math.random() * 255));
+    cv.drawContours(dst, contours, i, color, 1, cv.LINE_8, hierarchy, 100);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contours_more_functions_convexityDefects.html

@@ -0,0 +1,87 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Convexity Defects Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Convexity Defects Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 100, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let hull = new cv.Mat();
+let defect = new cv.Mat();
+let cnt = contours.get(0);
+let lineColor = new cv.Scalar(255, 0, 0);
+let circleColor = new cv.Scalar(255, 255, 255);
+cv.convexHull(cnt, hull, false, false);
+cv.convexityDefects(cnt, hull, defect);
+for (let i = 0; i < defect.rows; ++i) {
+    let start = new cv.Point(cnt.data32S[defect.data32S[i * 4] * 2],
+                             cnt.data32S[defect.data32S[i * 4] * 2 + 1]);
+    let end = new cv.Point(cnt.data32S[defect.data32S[i * 4 + 1] * 2],
+                           cnt.data32S[defect.data32S[i * 4 + 1] * 2 + 1]);
+    let far = new cv.Point(cnt.data32S[defect.data32S[i * 4 + 2] * 2],
+                           cnt.data32S[defect.data32S[i * 4 + 2] * 2 + 1]);
+    cv.line(dst, start, end, lineColor, 2, cv.LINE_AA, 0);
+    cv.circle(dst, far, 3, circleColor, -1);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); hierarchy.delete(); contours.delete(); hull.delete(); defect.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_contours_more_functions_shape.html

@@ -0,0 +1,82 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Match Shape Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Match Shape Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<div>
+    <p><strong>The result is: </strong><span id="matchShapesOutput"></span></p>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(src, src, 177, 200, cv.THRESH_BINARY);
+let contours = new cv.MatVector();
+let hierarchy = new cv.Mat();
+cv.findContours(src, contours, hierarchy, cv.RETR_CCOMP, cv.CHAIN_APPROX_SIMPLE);
+let contourID0 = 10;
+let contourID1 = 5;
+let color0 = new cv.Scalar(255, 0, 0);
+let color1 = new cv.Scalar(0, 0, 255);
+// You can try more different parameters
+let result = cv.matchShapes(contours.get(contourID0), contours.get(contourID1), 1, 0);
+matchShapesOutput.innerHTML = result;
+cv.drawContours(dst, contours, contourID0, color0, 1, cv.LINE_8, hierarchy, 100);
+cv.drawContours(dst, contours, contourID1, color1, 1, cv.LINE_8, hierarchy, 100);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); contours.delete(); hierarchy.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('coins.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_dnn_example_helper.js

@@ -0,0 +1,119 @@
+getBlobFromImage = function(inputSize, mean, std, swapRB, image) {
+    let mat;
+    if (typeof(image) === 'string') {
+        mat = cv.imread(image);
+    } else {
+        mat = image;
+    }
+
+    let matC3 = new cv.Mat(mat.matSize[0], mat.matSize[1], cv.CV_8UC3);
+    cv.cvtColor(mat, matC3, cv.COLOR_RGBA2BGR);
+    let input = cv.blobFromImage(matC3, std, new cv.Size(inputSize[0], inputSize[1]),
+                                 new cv.Scalar(mean[0], mean[1], mean[2]), swapRB);
+
+    matC3.delete();
+    return input;
+}
+
+loadLables = async function(labelsUrl) {
+    let response = await fetch(labelsUrl);
+    let label = await response.text();
+    label = label.split('\n');
+    return label;
+}
+
+loadModel = async function(e) {
+    return new Promise((resolve) => {
+        let file = e.target.files[0];
+        let path = file.name;
+        let reader = new FileReader();
+        reader.readAsArrayBuffer(file);
+        reader.onload = function(ev) {
+            if (reader.readyState === 2) {
+                let buffer = reader.result;
+                let data = new Uint8Array(buffer);
+                cv.FS_createDataFile('/', path, data, true, false, false);
+                resolve(path);
+            }
+        }
+    });
+}
+
+getTopClasses = function(probs, labels, topK = 3) {
+    probs = Array.from(probs);
+    let indexes = probs.map((prob, index) => [prob, index]);
+    let sorted = indexes.sort((a, b) => {
+        if (a[0] === b[0]) {return 0;}
+        return a[0] < b[0] ? -1 : 1;
+    });
+    sorted.reverse();
+    let classes = [];
+    for (let i = 0; i < topK; ++i) {
+        let prob = sorted[i][0];
+        let index = sorted[i][1];
+        let c = {
+            label: labels[index],
+            prob: (prob * 100).toFixed(2)
+        }
+        classes.push(c);
+    }
+    return classes;
+}
+
+loadImageToCanvas = function(e, canvasId) {
+    let files = e.target.files;
+    let imgUrl = URL.createObjectURL(files[0]);
+    let canvas = document.getElementById(canvasId);
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = imgUrl;
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+}
+
+drawInfoTable = async function(jsonUrl, divId) {
+    let response = await fetch(jsonUrl);
+    let json = await response.json();
+
+    let appendix = document.getElementById(divId);
+    for (key of Object.keys(json)) {
+        let h3 = document.createElement('h3');
+        h3.textContent = key + " model";
+        appendix.appendChild(h3);
+
+        let table = document.createElement('table');
+        let head_tr = document.createElement('tr');
+        for (head of Object.keys(json[key][0])) {
+            let th = document.createElement('th');
+            th.textContent = head;
+            th.style.border = "1px solid black";
+            head_tr.appendChild(th);
+        }
+        table.appendChild(head_tr)
+
+        for (model of json[key]) {
+            let tr = document.createElement('tr');
+            for (params of Object.keys(model)) {
+                let td = document.createElement('td');
+                td.style.border = "1px solid black";
+                if (params !== "modelUrl" && params !== "configUrl" && params !== "labelsUrl") {
+                    td.textContent = model[params];
+                    tr.appendChild(td);
+                } else {
+                    let a = document.createElement('a');
+                    let link = document.createTextNode('link');
+                    a.append(link);
+                    a.href = model[params];
+                    td.appendChild(a);
+                    tr.appendChild(td);
+                }
+            }
+            table.appendChild(tr);
+        }
+        table.style.width = "800px";
+        table.style.borderCollapse = "collapse";
+        appendix.appendChild(table);
+    }
+}

OpenCV/opencv/doc/js_tutorials/js_assets/js_example_style.css

@@ -0,0 +1,70 @@
+body, div, p {
+    font: 400 14px/22px Roboto,sans-serif;
+}
+canvas, img, video {
+    border: 1px solid black;
+}
+td {
+    padding: 10px 0px 0px 10px;
+    text-align: center;
+}
+button {
+    display: inline-block;
+    color: #fff;
+    background-color: #337ab7;
+    border-color: #2e6da4;
+    padding: 6px 12px;
+    margin-bottom: 0;
+    font-size: 14px;
+    font-weight: bold;
+    text-align: center;
+    white-space: nowrap;
+    vertical-align: middle;
+    -ms-touch-action: manipulation;
+    touch-action: manipulation;
+    cursor: pointer;
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+    background-image: none;
+    border: 1px solid transparent;
+    border-radius: 4px;
+}
+button[disabled] {
+    cursor: not-allowed;
+    filter: alpha(opacity=65);
+    -webkit-box-shadow: none;
+    box-shadow: none;
+    opacity: .65;
+}
+.control {
+    margin-bottom: 3px;
+}
+.err {
+    color: red;
+    font-weight: bold;
+}
+.caption {
+    margin: 0;
+    font-weight: bold;
+}
+.code {
+    padding: 4px 6px;
+    margin: 4px 8px 4px 2px;
+    background-color: #FBFCFD;
+    border: 1px solid #C4CFE5;
+    font-family: monospace, fixed;
+    font-size: 13px;
+    min-height: 13px;
+    line-height: 1.0;
+    text-wrap: unrestricted;
+    padding-bottom: 0px;
+    margin: 0px;
+}
+.hidden {
+    display: none;
+}
+.small {
+    max-width: 300px;
+}

OpenCV/opencv/doc/js_tutorials/js_assets/js_face_detection.html

@@ -0,0 +1,100 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Face Detection Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Face Detection Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let gray = new cv.Mat();
+cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY, 0);
+let faces = new cv.RectVector();
+let eyes = new cv.RectVector();
+let faceCascade = new cv.CascadeClassifier();
+let eyeCascade = new cv.CascadeClassifier();
+// load pre-trained classifiers
+faceCascade.load('haarcascade_frontalface_default.xml');
+eyeCascade.load('haarcascade_eye.xml');
+// detect faces
+let msize = new cv.Size(0, 0);
+faceCascade.detectMultiScale(gray, faces, 1.1, 3, 0, msize, msize);
+for (let i = 0; i < faces.size(); ++i) {
+    let roiGray = gray.roi(faces.get(i));
+    let roiSrc = src.roi(faces.get(i));
+    let point1 = new cv.Point(faces.get(i).x, faces.get(i).y);
+    let point2 = new cv.Point(faces.get(i).x + faces.get(i).width,
+                              faces.get(i).y + faces.get(i).height);
+    cv.rectangle(src, point1, point2, [255, 0, 0, 255]);
+    // detect eyes in face ROI
+    eyeCascade.detectMultiScale(roiGray, eyes);
+    for (let j = 0; j < eyes.size(); ++j) {
+        let point1 = new cv.Point(eyes.get(j).x, eyes.get(j).y);
+        let point2 = new cv.Point(eyes.get(j).x + eyes.get(j).width,
+                                  eyes.get(j).y + eyes.get(j).height);
+        cv.rectangle(roiSrc, point1, point2, [0, 0, 255, 255]);
+    }
+    roiGray.delete(); roiSrc.delete();
+}
+cv.imshow('canvasOutput', src);
+src.delete(); gray.delete(); faceCascade.delete();
+eyeCascade.delete(); faces.delete(); eyes.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    let eyeCascadeFile = 'haarcascade_eye.xml';
+    utils.createFileFromUrl(eyeCascadeFile, eyeCascadeFile, () => {
+        let faceCascadeFile = 'haarcascade_frontalface_default.xml';
+        utils.createFileFromUrl(faceCascadeFile, faceCascadeFile, () => {
+            tryIt.removeAttribute('disabled');
+        });
+    });
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_face_detection_camera.html

@@ -0,0 +1,142 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Face Detection Camera Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Face Detection Camera Example</h2>
+<p>
+    Click <b>Start/Stop</b> button to start or stop the camera capture.<br>
+    The <b>videoInput</b> is a &lt;video&gt; element used as face detector input.
+    The <b>canvasOutput</b> is a &lt;canvas&gt; element used as face detector output.<br>
+    The code of &lt;textarea&gt; will be executed when video is started.
+    You can modify the code to investigate more.
+</p>
+<div>
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<textarea class="code" rows="29" cols="80" id="codeEditor" spellcheck="false">
+</textarea>
+</div>
+<p class="err" id="errorMessage"></p>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <video id="videoInput" width=320 height=240></video>
+        </td>
+        <td>
+            <canvas id="canvasOutput" width=320 height=240></canvas>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">videoInput</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    </table>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let video = document.getElementById('videoInput');
+let src = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let dst = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let gray = new cv.Mat();
+let cap = new cv.VideoCapture(video);
+let faces = new cv.RectVector();
+let classifier = new cv.CascadeClassifier();
+
+// load pre-trained classifiers
+classifier.load('haarcascade_frontalface_default.xml');
+
+const FPS = 30;
+function processVideo() {
+    try {
+        if (!streaming) {
+            // clean and stop.
+            src.delete();
+            dst.delete();
+            gray.delete();
+            faces.delete();
+            classifier.delete();
+            return;
+        }
+        let begin = Date.now();
+        // start processing.
+        cap.read(src);
+        src.copyTo(dst);
+        cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
+        // detect faces.
+        classifier.detectMultiScale(gray, faces, 1.1, 3, 0);
+        // draw faces.
+        for (let i = 0; i < faces.size(); ++i) {
+            let face = faces.get(i);
+            let point1 = new cv.Point(face.x, face.y);
+            let point2 = new cv.Point(face.x + face.width, face.y + face.height);
+            cv.rectangle(dst, point1, point2, [255, 0, 0, 255]);
+        }
+        cv.imshow('canvasOutput', dst);
+        // schedule the next one.
+        let delay = 1000/FPS - (Date.now() - begin);
+        setTimeout(processVideo, delay);
+    } catch (err) {
+        utils.printError(err);
+    }
+};
+
+// schedule the first one.
+setTimeout(processVideo, 0);
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+
+let streaming = false;
+let videoInput = document.getElementById('videoInput');
+let startAndStop = document.getElementById('startAndStop');
+let canvasOutput = document.getElementById('canvasOutput');
+let canvasContext = canvasOutput.getContext('2d');
+
+startAndStop.addEventListener('click', () => {
+    if (!streaming) {
+        utils.clearError();
+        utils.startCamera('qvga', onVideoStarted, 'videoInput');
+    } else {
+        utils.stopCamera();
+        onVideoStopped();
+    }
+});
+
+function onVideoStarted() {
+    streaming = true;
+    startAndStop.innerText = 'Stop';
+    videoInput.width = videoInput.videoWidth;
+    videoInput.height = videoInput.videoHeight;
+    utils.executeCode('codeEditor');
+}
+
+function onVideoStopped() {
+    streaming = false;
+    canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
+    startAndStop.innerText = 'Start';
+}
+
+utils.loadOpenCv(() => {
+    let faceCascadeFile = 'haarcascade_frontalface_default.xml';
+    utils.createFileFromUrl(faceCascadeFile, faceCascadeFile, () => {
+        startAndStop.removeAttribute('disabled');
+    });
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_filtering_GaussianBlur.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Gaussian Blur Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Gaussian Blur Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let ksize = new cv.Size(3, 3);
+// You can try more different parameters
+cv.GaussianBlur(src, dst, ksize, 0, 0, cv.BORDER_DEFAULT);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_filtering_bilateralFilter.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Bilateral Filter Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Bilateral Filter Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGBA2RGB, 0);
+// You can try more different parameters
+cv.bilateralFilter(src, dst, 9, 75, 75, cv.BORDER_DEFAULT);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_filtering_blur.html

@@ -0,0 +1,70 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Blur Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Blur Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let ksize = new cv.Size(3, 3);
+let anchor = new cv.Point(-1, -1);
+// You can try more different parameters
+cv.blur(src, dst, ksize, anchor, cv.BORDER_DEFAULT);
+// cv.boxFilter(src, dst, -1, ksize, anchor, true, cv.BORDER_DEFAULT)
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_filtering_filter.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Filter Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Filter Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let M = cv.Mat.eye(3, 3, cv.CV_32FC1);
+let anchor = new cv.Point(-1, -1);
+// You can try more different parameters
+cv.filter2D(src, dst, cv.CV_8U, M, anchor, 0, cv.BORDER_DEFAULT);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_filtering_medianBlur.html

@@ -0,0 +1,67 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Median Blur Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Median Blur Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// You can try more different parameters
+cv.medianBlur(src, dst, 5);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_fourier_transform_dft.html

@@ -0,0 +1,128 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image DFT Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image DFT Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+
+// get optimal size of DFT
+let optimalRows = cv.getOptimalDFTSize(src.rows);
+let optimalCols = cv.getOptimalDFTSize(src.cols);
+let s0 = cv.Scalar.all(0);
+let padded = new cv.Mat();
+cv.copyMakeBorder(src, padded, 0, optimalRows - src.rows, 0,
+                  optimalCols - src.cols, cv.BORDER_CONSTANT, s0);
+
+// use cv.MatVector to distribute space for real part and imaginary part
+let plane0 = new cv.Mat();
+padded.convertTo(plane0, cv.CV_32F);
+let planes = new cv.MatVector();
+let complexI = new cv.Mat();
+let plane1 = new cv.Mat.zeros(padded.rows, padded.cols, cv.CV_32F);
+planes.push_back(plane0);
+planes.push_back(plane1);
+cv.merge(planes, complexI);
+
+// in-place dft transform
+cv.dft(complexI, complexI);
+
+// compute log(1 + sqrt(Re(DFT(img))**2 + Im(DFT(img))**2))
+cv.split(complexI, planes);
+cv.magnitude(planes.get(0), planes.get(1), planes.get(0));
+let mag = planes.get(0);
+let m1 = new cv.Mat.ones(mag.rows, mag.cols, mag.type());
+cv.add(mag, m1, mag);
+cv.log(mag, mag);
+
+// crop the spectrum, if it has an odd number of rows or columns
+let rect = new cv.Rect(0, 0, mag.cols & -2, mag.rows & -2);
+mag = mag.roi(rect);
+
+// rearrange the quadrants of Fourier image
+// so that the origin is at the image center
+let cx = mag.cols / 2;
+let cy = mag.rows / 2;
+let tmp = new cv.Mat();
+
+let rect0 = new cv.Rect(0, 0, cx, cy);
+let rect1 = new cv.Rect(cx, 0, cx, cy);
+let rect2 = new cv.Rect(0, cy, cx, cy);
+let rect3 = new cv.Rect(cx, cy, cx, cy);
+
+let q0 = mag.roi(rect0);
+let q1 = mag.roi(rect1);
+let q2 = mag.roi(rect2);
+let q3 = mag.roi(rect3);
+
+// exchange 1 and 4 quadrants
+q0.copyTo(tmp);
+q3.copyTo(q0);
+tmp.copyTo(q3);
+
+// exchange 2 and 3 quadrants
+q1.copyTo(tmp);
+q2.copyTo(q1);
+tmp.copyTo(q2);
+
+// The pixel value of cv.CV_32S type image ranges from 0 to 1.
+cv.normalize(mag, mag, 0, 1, cv.NORM_MINMAX);
+
+cv.imshow('canvasOutput', mag);
+src.delete(); padded.delete(); planes.delete(); complexI.delete(); m1.delete(); tmp.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_geometric_transformations_getAffineTransform.html

@@ -0,0 +1,74 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Get Affine Transform Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Get Affine Transform Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// (data32F[0], data32F[1]) is the first point
+// (data32F[2], data32F[3]) is the sescond point
+// (data32F[4], data32F[5]) is the third point
+let srcTri = cv.matFromArray(3, 1, cv.CV_32FC2, [0, 0, 0, 1, 1, 0]);
+let dstTri = cv.matFromArray(3, 1, cv.CV_32FC2, [0.6, 0.2, 0.1, 1.3, 1.5, 0.3]);
+let dsize = new cv.Size(src.rows, src.cols);
+let M = cv.getAffineTransform(srcTri, dstTri);
+// You can try more different parameters
+cv.warpAffine(src, dst, M, dsize, cv.INTER_LINEAR, cv.BORDER_CONSTANT, new cv.Scalar());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete(); srcTri.delete(); dstTri.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_geometric_transformations_resize.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Resize Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Resize Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let dsize = new cv.Size(300, 300);
+// You can try more different parameters
+cv.resize(src, dst, dsize, 0, 0, cv.INTER_AREA);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_geometric_transformations_rotateWarpAffine.html

@@ -0,0 +1,70 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Rotate Transform Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Rotate Transform Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let dsize = new cv.Size(src.rows, src.cols);
+let center = new cv.Point(src.cols / 2, src.rows / 2);
+// You can try more different parameters
+let M = cv.getRotationMatrix2D(center, 45, 1);
+cv.warpAffine(src, dst, M, dsize, cv.INTER_LINEAR, cv.BORDER_CONSTANT, new cv.Scalar());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_geometric_transformations_warpAffine.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Affine Transform Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Affine Transform Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let M = cv.matFromArray(2, 3, cv.CV_64FC1, [1, 0, 50, 0, 1, 100]);
+let dsize = new cv.Size(src.rows, src.cols);
+// You can try more different parameters
+cv.warpAffine(src, dst, M, dsize, cv.INTER_LINEAR, cv.BORDER_CONSTANT, new cv.Scalar());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_geometric_transformations_warpPerspective.html

@@ -0,0 +1,75 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Perspectiv Transform Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Perspectiv Transform Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let dsize = new cv.Size(src.rows, src.cols);
+// (data32F[0], data32F[1]) is the first point
+// (data32F[2], data32F[3]) is the sescond point
+// (data32F[4], data32F[5]) is the third point
+// (data32F[6], data32F[7]) is the fourth point
+let srcTri = cv.matFromArray(4, 1, cv.CV_32FC2, [56, 65, 368, 52, 28, 387, 389, 390]);
+let dstTri = cv.matFromArray(4, 1, cv.CV_32FC2, [0, 0, 300, 0, 0, 300, 300, 300]);
+let M = cv.getPerspectiveTransform(srcTri, dstTri);
+// You can try more different parameters
+cv.warpPerspective(src, dst, M, dsize, cv.INTER_LINEAR, cv.BORDER_CONSTANT, new cv.Scalar());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete(); srcTri.delete(); dstTri.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_grabcut_grabCut.html

@@ -0,0 +1,85 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image GrabCut Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image GrabCut Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2RGB, 0);
+let mask = new cv.Mat();
+let bgdModel = new cv.Mat();
+let fgdModel = new cv.Mat();
+let rect = new cv.Rect(50, 50, 260, 280);
+cv.grabCut(src, mask, rect, bgdModel, fgdModel, 1, cv.GC_INIT_WITH_RECT);
+// draw foreground
+for (let i = 0; i < src.rows; i++) {
+    for (let j = 0; j < src.cols; j++) {
+        if (mask.ucharPtr(i, j)[0] == 0 || mask.ucharPtr(i, j)[0] == 2) {
+            src.ucharPtr(i, j)[0] = 0;
+            src.ucharPtr(i, j)[1] = 0;
+            src.ucharPtr(i, j)[2] = 0;
+        }
+    }
+}
+// draw grab rect
+let color = new cv.Scalar(0, 0, 255);
+let point1 = new cv.Point(rect.x, rect.y);
+let point2 = new cv.Point(rect.x + rect.width, rect.y + rect.height);
+cv.rectangle(src, point1, point2, color);
+cv.imshow('canvasOutput', src);
+src.delete(); mask.delete(); bgdModel.delete(); fgdModel.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_gradients_Laplacian.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Laplacian Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Laplacian Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGB2GRAY, 0);
+// You can try more different parameters
+cv.Laplacian(src, dst, cv.CV_8U, 1, 1, 0, cv.BORDER_DEFAULT);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_gradients_Sobel.html

@@ -0,0 +1,79 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Sobel Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Sobel Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b>, <b>canvasOutputx</b> and <b>canvasOutputy</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput" class="small"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutputx" class="small"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutputy" class="small"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutputx</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutputy</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dstx = new cv.Mat();
+let dsty = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGB2GRAY, 0);
+// You can try more different parameters
+cv.Sobel(src, dstx, cv.CV_8U, 1, 0, 3, 1, 0, cv.BORDER_DEFAULT);
+cv.Sobel(src, dsty, cv.CV_8U, 0, 1, 3, 1, 0, cv.BORDER_DEFAULT);
+// cv.Scharr(src, dstx, cv.CV_8U, 1, 0, 1, 0, cv.BORDER_DEFAULT);
+// cv.Scharr(src, dsty, cv.CV_8U, 0, 1, 1, 0, cv.BORDER_DEFAULT);
+cv.imshow('canvasOutputx', dstx);
+cv.imshow('canvasOutputy', dsty);
+src.delete(); dstx.delete(); dsty.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_gradients_absSobel.html

@@ -0,0 +1,78 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image AbsSobel Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image AbsSobel Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b>, <b>canvasOutput8U</b> and <b>canvasOutput64F</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput" class="small"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput8U" class="small"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput64F" class="small"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput8U</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput64F</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dstx = new cv.Mat();
+let absDstx = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGB2GRAY, 0);
+// You can try more different parameters
+cv.Sobel(src, dstx, cv.CV_8U, 1, 0, 3, 1, 0, cv.BORDER_DEFAULT);
+cv.Sobel(src, absDstx, cv.CV_64F, 1, 0, 3, 1, 0, cv.BORDER_DEFAULT);
+cv.convertScaleAbs(absDstx, absDstx, 1, 0);
+cv.imshow('canvasOutput8U', dstx);
+cv.imshow('canvasOutput64F', absDstx);
+src.delete(); dstx.delete(); absDstx.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_histogram_backprojection_calcBackProject.html

@@ -0,0 +1,90 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Back Project Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Back Project Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>srcCanvasInput</b>, <b>dstCanvasInput</b> and <b>canvasInput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="srcCanvasInput" class="small"></canvas>
+        </td>
+        <td>
+            <canvas id="dstCanvasInput" class="small"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput" class="small"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">srcCanvasInput <input type="file" id="srcFileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">dstCanvasInput <input type="file" id="dstFileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('srcCanvasInput');
+let dst = cv.imread('dstCanvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGB2HSV, 0);
+cv.cvtColor(dst, dst, cv.COLOR_RGB2HSV, 0);
+let srcVec = new cv.MatVector();
+let dstVec = new cv.MatVector();
+srcVec.push_back(src); dstVec.push_back(dst);
+let backproj = new cv.Mat();
+let none = new cv.Mat();
+let mask = new cv.Mat();
+let hist = new cv.Mat();
+let channels = [0];
+let histSize = [50];
+let ranges = [0, 180];
+let accumulate = false;
+cv.calcHist(srcVec, channels, mask, hist, histSize, ranges, accumulate);
+cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX, -1, none);
+cv.calcBackProject(dstVec, channels, hist, backproj, ranges, 1);
+cv.imshow('canvasOutput', backproj);
+src.delete(); dst.delete(); srcVec.delete(); dstVec.delete();
+backproj.delete(); mask.delete(); hist.delete(); none.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('handSrc.jpg', 'srcCanvasInput');
+utils.loadImageToCanvas('handDst.jpg', 'dstCanvasInput');
+utils.addFileInputHandler('srcFileInput', 'srcCanvasInput');
+utils.addFileInputHandler('dstFileInput', 'dstCanvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_histogram_begins_calcHist.html

@@ -0,0 +1,88 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Histogram Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Histogram Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+let srcVec = new cv.MatVector();
+srcVec.push_back(src);
+let accumulate = false;
+let channels = [0];
+let histSize = [256];
+let ranges = [0, 255];
+let hist = new cv.Mat();
+let mask = new cv.Mat();
+let color = new cv.Scalar(255, 255, 255);
+let scale = 2;
+// You can try more different parameters
+cv.calcHist(srcVec, channels, mask, hist, histSize, ranges, accumulate);
+let result = cv.minMaxLoc(hist, mask);
+let max = result.maxVal;
+let dst = new cv.Mat.zeros(src.rows, histSize[0] * scale,
+                           cv.CV_8UC3);
+// draw histogram
+for (let i = 0; i < histSize[0]; i++) {
+    let binVal = hist.data32F[i] * src.rows / max;
+    let point1 = new cv.Point(i * scale, src.rows - 1);
+    let point2 = new cv.Point((i + 1) * scale - 1, src.rows - binVal);
+    cv.rectangle(dst, point1, point2, color, cv.FILLED);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); srcVec.delete(); mask.delete(); hist.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_histogram_equalization_createCLAHE.html

@@ -0,0 +1,73 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image CLAHE Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image CLAHE Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let equalDst = new cv.Mat();
+let claheDst = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.equalizeHist(src, equalDst);
+let tileGridSize = new cv.Size(8, 8);
+// You can try more different parameters
+let clahe = new cv.CLAHE(40, tileGridSize);
+clahe.apply(src, claheDst);
+cv.imshow('canvasOutput', equalDst);
+cv.imshow('canvasOutput', claheDst);
+src.delete(); equalDst.delete(); claheDst.delete(); clahe.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_histogram_equalization_equalizeHist.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Equalize Histogram Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Equalize Histogram Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.equalizeHist(src, dst);
+cv.imshow('canvasOutput', src);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_houghcircles_HoughCirclesP.html

@@ -0,0 +1,79 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Hough Circles Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Hough Circles Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8U);
+let circles = new cv.Mat();
+let color = new cv.Scalar(255, 0, 0);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+// You can try more different parameters
+cv.HoughCircles(src, circles, cv.HOUGH_GRADIENT,
+                1, 45, 75, 40, 0, 0);
+// draw circles
+for (let i = 0; i < circles.cols; ++i) {
+    let x = circles.data32F[i * 3];
+    let y = circles.data32F[i * 3 + 1];
+    let radius = circles.data32F[i * 3 + 2];
+    let center = new cv.Point(x, y);
+    cv.circle(dst, center, radius, color);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); circles.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('coins.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_houghlines_HoughLines.html

@@ -0,0 +1,83 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Hough Lines Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Hough Lines Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+let lines = new cv.Mat();
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.Canny(src, src, 50, 200, 3);
+// You can try more different parameters
+cv.HoughLines(src, lines, 1, Math.PI / 180,
+              30, 0, 0, 0, Math.PI);
+// draw lines
+for (let i = 0; i < lines.rows; ++i) {
+    let rho = lines.data32F[i * 2];
+    let theta = lines.data32F[i * 2 + 1];
+    let a = Math.cos(theta);
+    let b = Math.sin(theta);
+    let x0 = a * rho;
+    let y0 = b * rho;
+    let startPoint = {x: x0 - 1000 * b, y: y0 + 1000 * a};
+    let endPoint = {x: x0 + 1000 * b, y: y0 - 1000 * a};
+    cv.line(dst, startPoint, endPoint, [255, 0, 0, 255]);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); lines.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_houghlines_HoughLinesP.html

@@ -0,0 +1,77 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image HoughLinesP Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image HoughLinesP Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = cv.Mat.zeros(src.rows, src.cols, cv.CV_8UC3);
+let lines = new cv.Mat();
+let color = new cv.Scalar(255, 0, 0);
+cv.cvtColor(src, src, cv.COLOR_RGBA2GRAY, 0);
+cv.Canny(src, src, 50, 200, 3);
+// You can try more different parameters
+cv.HoughLinesP(src, lines, 1, Math.PI / 180, 2, 0, 0);
+// draw lines
+for (let i = 0; i < lines.rows; ++i) {
+    let startPoint = new cv.Point(lines.data32S[i * 4], lines.data32S[i * 4 + 1]);
+    let endPoint = new cv.Point(lines.data32S[i * 4 + 2], lines.data32S[i * 4 + 3]);
+    cv.line(dst, startPoint, endPoint, color);
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); lines.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_image_arithmetics_bitwise.html

@@ -0,0 +1,114 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Bitwise Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Bitwise Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>imageCanvasInput</b>, <b>logoCanvasInput</b> and <b>CanvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="imageCanvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">imageCanvasInput <input type="file" id="imageFileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <canvas id="logoCanvasInput"></canvas>
+        </td>
+        <td>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">logoCanvasInput <input type="file" id="logoFileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('imageCanvasInput');
+let logo = cv.imread('logoCanvasInput');
+let dst = new cv.Mat();
+let roi = new cv.Mat();
+let mask = new cv.Mat();
+let maskInv = new cv.Mat();
+let imgBg = new cv.Mat();
+let imgFg = new cv.Mat();
+let sum = new cv.Mat();
+let rect = new cv.Rect(0, 0, logo.cols, logo.rows);
+
+// I want to put logo on top-left corner, So I create a ROI
+roi = src.roi(rect);
+
+// Create a mask of logo and create its inverse mask also
+cv.cvtColor(logo, mask, cv.COLOR_RGBA2GRAY, 0);
+cv.threshold(mask, mask, 100, 255, cv.THRESH_BINARY);
+cv.bitwise_not(mask, maskInv);
+
+// Black-out the area of logo in ROI
+cv.bitwise_and(roi, roi, imgBg, maskInv);
+
+// Take only region of logo from logo image
+cv.bitwise_and(logo, logo, imgFg, mask);
+
+// Put logo in ROI and modify the main image
+cv.add(imgBg, imgFg, sum);
+
+dst = src.clone();
+for (let i = 0; i < logo.rows; i++) {
+    for (let j = 0; j < logo.cols; j++) {
+        dst.ucharPtr(i, j)[0] = sum.ucharPtr(i, j)[0];
+    }
+}
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); logo.delete(); roi.delete(); mask.delete();
+maskInv.delete(); imgBg.delete(); imgFg.delete(); sum.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'imageCanvasInput');
+utils.loadImageToCanvas('lenaFace.png', 'logoCanvasInput');
+utils.addFileInputHandler('imageFileInput', 'imageCanvasInput');
+utils.addFileInputHandler('logoFileInput', 'logoCanvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_image_classification.html

@@ -0,0 +1,263 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Image Classification Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Image Classification Example</h2>
+<p>
+    This tutorial shows you how to write an image classification example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <table style="visibility: hidden;" id="result">
+                    <thead>
+                        <tr>
+                            <th scope="col">#</th>
+                            <th scope="col" width=300>Label</th>
+                            <th scope="col">Probability</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <th scope="row">1</th>
+                            <td id="label0" align="center"></td>
+                            <td id="prob0" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">2</th>
+                            <td id="label1" align="center"></td>
+                            <td id="prob1" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">3</th>
+                            <td id="label2" align="center"></td>
+                            <td id="prob2" align="center"></td>
+                        </tr>
+                    </tbody>
+                </table>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td></td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [224,224];
+mean = [104, 117, 123];
+std = 1;
+swapRB = false;
+
+// record if need softmax function for post-processing
+needSoftmax = false;
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const probs = softmax(result);
+    const classes = getTopClasses(probs, labels);
+
+    updateResult(classes, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+softmax = function(result) {
+    let arr = result.data32F;
+    if (needSoftmax) {
+        const maxNum = Math.max(...arr);
+        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
+        return arr.map((value, index) => {
+            return Math.exp(value - maxNum) / expSum;
+        });
+    } else {
+        return arr;
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_image_classification_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
+    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'space_shuttle.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var softmax = function(result){};
+    var getTopClasses = function(mat, labels, topK = 3){};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function updateResult(classes, time) {
+        try{
+            classes.forEach((c,i) => {
+                let labelElement = document.getElementById('label'+i);
+                let probElement = document.getElementById('prob'+i);
+                labelElement.innerHTML = c.label;
+                probElement.innerHTML = c.prob + '%';
+            });
+            let result = document.getElementById('result');
+            result.style.visibility = 'visible';
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('result').style.visibility = 'hidden';
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_image_classification_model_info.json

@@ -0,0 +1,65 @@
+{
+    "caffe": [
+        {
+            "model": "alexnet",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt"
+        },
+        {
+            "model": "densenet",
+            "mean": "127.5, 127.5, 127.5",
+            "std": "0.007843",
+            "swapRB": "false",
+            "needSoftmax": "true",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "https://drive.google.com/open?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE",
+            "configUrl": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt"
+        },
+        {
+            "model": "googlenet",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt"
+        },
+        {
+            "model": "squeezenet",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/deploy.prototxt"
+        },
+        {
+            "model": "VGG",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel",
+            "configUrl": "https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt"
+        }
+    ],
+    "tensorflow": [
+        {
+            "model": "inception",
+            "mean": "123, 117, 104",
+            "std": "1",
+            "swapRB": "true",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/imagenet_comp_graph_label_strings.txt",
+            "modelUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/tensorflow_inception_graph.pb"
+        }
+    ]
+}

OpenCV/opencv/doc/js_tutorials/js_assets/js_image_classification_webnn_polyfill.html

@@ -0,0 +1,269 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Image Classification Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+    <script src="./webnn-polyfill.js"></script>
+</head>
+
+<body>
+<h2>Image Classification Example</h2>
+<p>
+    This tutorial shows you how to write an image classification example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <table style="visibility: hidden;" id="result">
+                    <thead>
+                        <tr>
+                            <th scope="col">#</th>
+                            <th scope="col" width=300>Label</th>
+                            <th scope="col">Probability</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <th scope="row">1</th>
+                            <td id="label0" align="center"></td>
+                            <td id="prob0" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">2</th>
+                            <td id="label1" align="center"></td>
+                            <td id="prob1" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">3</th>
+                            <td id="label2" align="center"></td>
+                            <td id="prob2" align="center"></td>
+                        </tr>
+                    </tbody>
+                </table>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td></td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [224,224];
+mean = [104, 117, 123];
+std = 1;
+swapRB = false;
+
+// record if need softmax function for post-processing
+needSoftmax = false;
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setPreferableBackend(6);
+    net.setInput(input);
+    let result = net.forward();
+    const start = performance.now();
+    for (i=0;i<200;i++)
+    {
+        result = net.forward();
+    }
+    const time  = performance.now()-start;
+    const probs = softmax(result);
+    const classes = getTopClasses(probs, labels);
+
+    updateResult(classes, time/200);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+softmax = function(result) {
+    let arr = result.data32F;
+    if (needSoftmax) {
+        const maxNum = Math.max(...arr);
+        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
+        return arr.map((value, index) => {
+            return Math.exp(value - maxNum) / expSum;
+        });
+    } else {
+        return arr;
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_image_classification_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
+    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'space_shuttle.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var softmax = function(result){};
+    var getTopClasses = function(mat, labels, topK = 3){};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function updateResult(classes, time) {
+        try{
+            classes.forEach((c,i) => {
+                let labelElement = document.getElementById('label'+i);
+                let probElement = document.getElementById('prob'+i);
+                labelElement.innerHTML = c.label;
+                probElement.innerHTML = c.prob + '%';
+            });
+            let result = document.getElementById('result');
+            result.style.visibility = 'visible';
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('result').style.visibility = 'hidden';
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_image_classification_with_camera.html

@@ -0,0 +1,281 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Image Classification Example with Camera</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Image Classification Example with Camera</h2>
+<p>
+    This tutorial shows you how to write an image classification example with camera.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
+</p>
+
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <video id="videoInput" width="400" height="400"></video>
+            </td>
+            <td>
+                <table style="visibility: hidden;" id="result">
+                    <thead>
+                        <tr>
+                            <th scope="col">#</th>
+                            <th scope="col" width=300>Label</th>
+                            <th scope="col">Probability</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <th scope="row">1</th>
+                            <td id="label0" align="center"></td>
+                            <td id="prob0" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">2</th>
+                            <td id="label1" align="center"></td>
+                            <td id="prob1" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">3</th>
+                            <td id="label2" align="center"></td>
+                            <td id="prob2" align="center"></td>
+                        </tr>
+                    </tbody>
+                </table>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    videoInput
+                </div>
+            </td>
+            <td></td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [224,224];
+mean = [104, 117, 123];
+std = 1;
+swapRB = false;
+
+// record if need softmax function for post-processing
+needSoftmax = false;
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/classification_classes_ILSVRC2012.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let cap = new cv.VideoCapture(video);
+
+main = async function(frame) {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const probs = softmax(result);
+    const classes = getTopClasses(probs, labels);
+
+    updateResult(classes, time);
+    setTimeout(processVideo, 0);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+
+function processVideo() {
+    try {
+        if (!streaming) {
+            return;
+        }
+        cap.read(frame);
+        main(frame);
+    } catch (err) {
+        utils.printError(err);
+    }
+}
+
+setTimeout(processVideo, 0);
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+softmax = function(result) {
+    let arr = result.data32F;
+    if (needSoftmax) {
+        const maxNum = Math.max(...arr);
+        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
+        return arr.map((value, index) => {
+            return Math.exp(value - maxNum) / expSum;
+        });
+    } else {
+        return arr;
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_image_classification_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
+    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
+
+    let video = document.getElementById('videoInput');
+    let streaming = false;
+    let startAndStop = document.getElementById('startAndStop');
+    startAndStop.addEventListener('click', () => {
+        if (!streaming) {
+            utils.clearError();
+            utils.startCamera('qvga', onVideoStarted, 'videoInput');
+        } else {
+            utils.stopCamera();
+            onVideoStopped();
+        }
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        startAndStop.removeAttribute('disabled');
+
+    });
+
+    var main = async function(frame) {};
+    var softmax = function(result){};
+    var getTopClasses = function(mat, labels, topK = 3){};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function onVideoStarted() {
+        streaming = true;
+        startAndStop.innerText = 'Stop';
+        videoInput.width = videoInput.videoWidth;
+        videoInput.height = videoInput.videoHeight;
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+    }
+
+    function onVideoStopped() {
+        streaming = false;
+        startAndStop.innerText = 'Start';
+        initStatus();
+    }
+
+    function updateResult(classes, time) {
+        try{
+            classes.forEach((c,i) => {
+                let labelElement = document.getElementById('label'+i);
+                let probElement = document.getElementById('prob'+i);
+                labelElement.innerHTML = c.label;
+                probElement.innerHTML = c.prob + '%';
+            });
+            let result = document.getElementById('result');
+            result.style.visibility = 'visible';
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('result').style.visibility = 'hidden';
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_image_display.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Read and Show Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Read and Show Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="8" cols="80" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// To distinguish the input and output, we graying the image.
+// You can try different conversions.
+cv.cvtColor(src, dst, cv.COLOR_RGBA2GRAY);
+cv.imshow('canvasOutput', dst);
+src.delete();
+dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_imgproc_camera.html

@@ -0,0 +1,700 @@
+<!DOCTYPE html>
+<html >
+<head>
+<meta charset="utf-8">
+<title>Image Processing Video Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+<style type="text/css">
+.dg {
+    text-align: left;
+}
+.dg .property-name {
+    font: 11px Lucida Grande,sans-serif;
+    line-height: 27px;
+}
+.dg.main .close-button {
+    font: 11px Lucida Grande,sans-serif;
+    line-height: 27px;
+}
+.cell-top {
+    vertical-align: top;
+}
+</style>
+</head>
+<body>
+<h2>Image Processing Video Example</h2>
+<p>
+    Open the controls and try different image processing filters.
+</p>
+<p class="err" id="errorMessage"></p>
+<div id="container">
+    <table>
+        <tr>
+            <td></td>
+            <td>
+                <div>
+                    <span>Current Filter: </span><span id="filterName">Pass Through</span>
+                </div>
+            </td>
+            <td>
+                <div>Select Filter:</div>
+            </td>
+            <td></td>
+        </tr>
+        <tr>
+            <td></td>
+            <td class="cell-top">
+                <canvas id="canvasOutput" width="640" height="480"></canvas>
+            </td>
+            <td class="cell-top">
+                <div id="guiContainer"></div>
+            </td>
+            <td></td>
+        </tr>
+    </table>
+    <div>
+        <video id="videoInput" class="hidden">Your browser does not support the video tag.</video>
+    </div>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/stats.js/r16/Stats.min.js" type="text/javascript"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/dat-gui/0.6.4/dat.gui.min.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+let width = 0;
+let height = 0;
+
+let resolution = window.innerWidth < 960 ? 'qvga' : 'vga';
+
+// whether streaming video from the camera.
+let streaming = false;
+
+let video = document.getElementById('videoInput');
+let vc = null;
+
+let container = document.getElementById('container');
+
+let lastFilter = '';
+let src = null;
+let dstC1 = null;
+let dstC3 = null;
+let dstC4 = null;
+
+function startVideoProcessing() {
+    src = new cv.Mat(height, width, cv.CV_8UC4);
+    dstC1 = new cv.Mat(height, width, cv.CV_8UC1);
+    dstC3 = new cv.Mat(height, width, cv.CV_8UC3);
+    dstC4 = new cv.Mat(height, width, cv.CV_8UC4);
+    requestAnimationFrame(processVideo);
+}
+
+function passThrough(src) {
+    return src;
+}
+
+function gray(src) {
+    cv.cvtColor(src, dstC1, cv.COLOR_RGBA2GRAY);
+    return dstC1;
+}
+
+function hsv(src) {
+    cv.cvtColor(src, dstC3, cv.COLOR_RGBA2RGB);
+    cv.cvtColor(dstC3, dstC3, cv.COLOR_RGB2HSV);
+    return dstC3;
+}
+
+function canny(src) {
+    cv.cvtColor(src, dstC1, cv.COLOR_RGBA2GRAY);
+    cv.Canny(dstC1, dstC1, controls.cannyThreshold1, controls.cannyThreshold2,
+             controls.cannyApertureSize, controls.cannyL2Gradient);
+    return dstC1;
+}
+
+function inRange(src) {
+    let lowValue = controls.inRangeLow;
+    let lowScalar = new cv.Scalar(lowValue, lowValue, lowValue, 255);
+    let highValue = controls.inRangeHigh;
+    let highScalar = new cv.Scalar(highValue, highValue, highValue, 255);
+    let low = new cv.Mat(height, width, src.type(), lowScalar);
+    let high = new cv.Mat(height, width, src.type(), highScalar);
+    cv.inRange(src, low, high, dstC1);
+    low.delete(); high.delete();
+    return dstC1;
+}
+
+function threshold(src) {
+    cv.threshold(src, dstC4, controls.thresholdValue, 200, cv.THRESH_BINARY);
+    return dstC4;
+}
+
+function adaptiveThreshold(src) {
+    let mat = new cv.Mat(height, width, cv.CV_8U);
+    cv.cvtColor(src, mat, cv.COLOR_RGBA2GRAY);
+    cv.adaptiveThreshold(mat, dstC1, 200, cv.ADAPTIVE_THRESH_GAUSSIAN_C,
+                         cv.THRESH_BINARY, Number(controls.adaptiveBlockSize), 2);
+    mat.delete();
+    return dstC1;
+}
+
+function gaussianBlur(src) {
+    cv.GaussianBlur(src, dstC4,
+                    {width: controls.gaussianBlurSize, height: controls.gaussianBlurSize},
+                    0, 0, cv.BORDER_DEFAULT);
+    return dstC4;
+}
+
+function bilateralFilter(src) {
+    let mat = new cv.Mat(height, width, cv.CV_8UC3);
+    cv.cvtColor(src, mat, cv.COLOR_RGBA2RGB);
+    cv.bilateralFilter(mat, dstC3, controls.bilateralFilterDiameter, controls.bilateralFilterSigma,
+                       controls.bilateralFilterSigma, cv.BORDER_DEFAULT);
+    mat.delete();
+    return dstC3;
+}
+
+function medianBlur(src) {
+    cv.medianBlur(src, dstC4, controls.medianBlurSize);
+    return dstC4;
+}
+
+function sobel(src) {
+    let mat = new cv.Mat(height, width, cv.CV_8UC1);
+    cv.cvtColor(src, mat, cv.COLOR_RGB2GRAY, 0);
+    cv.Sobel(mat, dstC1, cv.CV_8U, 1, 0, controls.sobelSize, 1, 0, cv.BORDER_DEFAULT);
+    mat.delete();
+    return dstC1;
+}
+
+function scharr(src) {
+    let mat = new cv.Mat(height, width, cv.CV_8UC1);
+    cv.cvtColor(src, mat, cv.COLOR_RGB2GRAY, 0);
+    cv.Scharr(mat, dstC1, cv.CV_8U, 1, 0, 1, 0, cv.BORDER_DEFAULT);
+    mat.delete();
+    return dstC1;
+}
+
+function laplacian(src) {
+    let mat = new cv.Mat(height, width, cv.CV_8UC1);
+    cv.cvtColor(src, mat, cv.COLOR_RGB2GRAY);
+    cv.Laplacian(mat, dstC1, cv.CV_8U, controls.laplacianSize, 1, 0, cv.BORDER_DEFAULT);
+    mat.delete();
+    return dstC1;
+}
+
+let contoursColor = [];
+for (let i = 0; i < 10000; i++) {
+    contoursColor.push([Math.round(Math.random() * 255),
+                        Math.round(Math.random() * 255),
+                        Math.round(Math.random() * 255), 0]);
+}
+
+function contours(src) {
+    cv.cvtColor(src, dstC1, cv.COLOR_RGBA2GRAY);
+    cv.threshold(dstC1, dstC4, 120, 200, cv.THRESH_BINARY);
+    let contours = new cv.MatVector();
+    let hierarchy = new cv.Mat();
+    cv.findContours(dstC4, contours, hierarchy,
+                    Number(controls.contoursMode),
+                    Number(controls.contoursMethod), {x: 0, y: 0});
+    dstC3.delete();
+    dstC3 = cv.Mat.ones(height, width, cv.CV_8UC3);
+    for (let i = 0; i<contours.size(); ++i) {
+        let color = contoursColor[i];
+        cv.drawContours(dstC3, contours, i, color, 1, cv.LINE_8, hierarchy);
+    }
+    contours.delete(); hierarchy.delete();
+    return dstC3;
+}
+
+function calcHist(src) {
+    cv.cvtColor(src, dstC1, cv.COLOR_RGBA2GRAY);
+    let srcVec = new cv.MatVector();
+    srcVec.push_back(dstC1);
+    let scale = 2;
+    let channels = [0];
+    let histSize = [src.cols/scale];
+    const ranges = [0, 255];
+    let hist = new cv.Mat();
+    let mask = new cv.Mat();
+    let color = new cv.Scalar(0xfb, 0xca, 0x04, 0xff);
+    cv.calcHist(srcVec, channels, mask, hist, histSize, ranges);
+    let result = cv.minMaxLoc(hist, mask);
+    let max = result.maxVal;
+    cv.cvtColor(dstC1, dstC4, cv.COLOR_GRAY2RGBA);
+    // draw histogram on src
+    for (let i = 0; i < histSize[0]; i++) {
+        let binVal = hist.data32F[i] * src.rows / max;
+        cv.rectangle(dstC4, {x: i * scale, y: src.rows - 1},
+                     {x: (i + 1) * scale - 1, y: src.rows - binVal/3}, color, cv.FILLED);
+    }
+    srcVec.delete();
+    mask.delete();
+    hist.delete();
+    return dstC4;
+}
+
+function equalizeHist(src) {
+    cv.cvtColor(src, dstC1, cv.COLOR_RGBA2GRAY, 0);
+    cv.equalizeHist(dstC1, dstC1);
+    return dstC1;
+}
+
+let base;
+
+function backprojection(src) {
+    if (lastFilter !== 'backprojection') {
+        if (base instanceof cv.Mat) {
+            base.delete();
+        }
+        base = src.clone();
+        cv.cvtColor(base, base, cv.COLOR_RGB2HSV, 0);
+    }
+    cv.cvtColor(src, dstC3, cv.COLOR_RGB2HSV, 0);
+    let baseVec = new cv.MatVector();
+    let targetVec = new cv.MatVector();
+    baseVec.push_back(base); targetVec.push_back(dstC3);
+    let mask = new cv.Mat();
+    let hist = new cv.Mat();
+    let channels = [0];
+    let histSize = [50];
+    let ranges;
+    if (controls.backprojectionRangeLow < controls.backprojectionRangeHigh) {
+        ranges = [controls.backprojectionRangeLow, controls.backprojectionRangeHigh];
+    } else {
+        return src;
+    }
+    cv.calcHist(baseVec, channels, mask, hist, histSize, ranges);
+    cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX);
+    cv.calcBackProject(targetVec, channels, hist, dstC1, ranges, 1);
+    baseVec.delete();
+    targetVec.delete();
+    mask.delete();
+    hist.delete();
+    return dstC1;
+}
+
+function erosion(src) {
+    let kernelSize = controls.erosionSize;
+    let kernel = cv.Mat.ones(kernelSize, kernelSize, cv.CV_8U);
+    let color = new cv.Scalar();
+    cv.erode(src, dstC4, kernel, {x: -1, y: -1}, 1, Number(controls.erosionBorderType), color);
+    kernel.delete();
+    return dstC4;
+}
+
+function dilation(src) {
+    let kernelSize = controls.dilationSize;
+    let kernel = cv.Mat.ones(kernelSize, kernelSize, cv.CV_8U);
+    let color = new cv.Scalar();
+    cv.dilate(src, dstC4, kernel, {x: -1, y: -1}, 1, Number(controls.dilationBorderType), color);
+    kernel.delete();
+    return dstC4;
+}
+
+function morphology(src) {
+    let kernelSize = controls.morphologySize;
+    let kernel = cv.getStructuringElement(Number(controls.morphologyShape),
+                                          {width: kernelSize, height: kernelSize});
+    let color = new cv.Scalar();
+    let op = Number(controls.morphologyOp);
+    let image = src;
+    if (op === cv.MORPH_GRADIENT || op === cv.MORPH_TOPHAT || op === cv.MORPH_BLACKHAT) {
+        cv.cvtColor(src, dstC3, cv.COLOR_RGBA2RGB);
+        image = dstC3;
+    }
+    cv.morphologyEx(image, dstC4, op, kernel, {x: -1, y: -1}, 1,
+                    Number(controls.morphologyBorderType), color);
+    kernel.delete();
+    return dstC4;
+}
+
+function processVideo() {
+    if (!streaming) return;
+    stats.begin();
+    vc.read(src);
+    let result;
+    switch (controls.filter) {
+        case 'passThrough': result = passThrough(src); break;
+        case 'gray': result = gray(src); break;
+        case 'hsv': result = hsv(src); break;
+        case 'canny': result = canny(src); break;
+        case 'inRange': result = inRange(src); break;
+        case 'threshold': result = threshold(src); break;
+        case 'adaptiveThreshold': result = adaptiveThreshold(src); break;
+        case 'gaussianBlur': result = gaussianBlur(src); break;
+        case 'bilateralFilter': result = bilateralFilter(src); break;
+        case 'medianBlur': result = medianBlur(src); break;
+        case 'sobel': result = sobel(src); break;
+        case 'scharr': result = scharr(src); break;
+        case 'laplacian': result = laplacian(src); break;
+        case 'contours': result = contours(src); break;
+        case 'calcHist': result = calcHist(src); break;
+        case 'equalizeHist': result = equalizeHist(src); break;
+        case 'backprojection': result = backprojection(src); break;
+        case 'erosion': result = erosion(src); break;
+        case 'dilation': result = dilation(src); break;
+        case 'morphology': result = morphology(src); break;
+        default: result = passThrough(src);
+    }
+    cv.imshow('canvasOutput', result);
+    stats.end();
+    lastFilter = controls.filter;
+    requestAnimationFrame(processVideo);
+}
+
+let stats = null;
+
+let filters = {
+    'passThrough': 'Pass Through',
+    'gray': 'Gray',
+    'hsv': 'HSV',
+    'canny': 'Canny Edge Detection',
+    'inRange': 'In Range',
+    'threshold': 'Threshold',
+    'adaptiveThreshold': 'Adaptive Threshold',
+    'gaussianBlur': 'Gaussian Blurring',
+    'medianBlur': 'Median Blurring',
+    'bilateralFilter': 'Bilateral Filtering',
+    'sobel': 'Sobel Derivatives',
+    'scharr': 'Scharr Derivatives',
+    'laplacian': 'Laplacian Derivatives',
+    'contours': 'Contours',
+    'calcHist': 'Calculation',
+    'equalizeHist': 'Equalization',
+    'backprojection': 'Backprojection',
+    'erosion': 'Erosion',
+    'dilation': 'Dilation',
+    'morphology': 'Morphology',
+};
+
+let filterName = document.getElementById('filterName');
+
+let controls;
+
+function initUI() {
+    stats = new Stats();
+    stats.showPanel(0);
+    container.appendChild(stats.domElement);
+    stats.domElement.style.position = 'absolute';
+    stats.domElement.style.right = '0px';
+    stats.domElement.style.top = '0px';
+
+    controls = {
+        filter: 'passThrough',
+        setFilter: function(filter) {
+            this.filter = filter;
+            filterName.innerHTML = filters[filter];
+        },
+        passThrough: function() {
+            this.setFilter('passThrough');
+        },
+        gray: function() {
+            this.setFilter('gray');
+        },
+        hsv: function() {
+            this.setFilter('hsv');
+        },
+        inRange: function() {
+            this.setFilter('inRange');
+        },
+        inRangeLow: 75,
+        inRangeHigh: 150,
+        threshold: function() {
+            this.setFilter('threshold');
+        },
+        thresholdValue: 100,
+        adaptiveThreshold: function() {
+            this.setFilter('adaptiveThreshold');
+        },
+        adaptiveBlockSize: 3,
+        gaussianBlur: function() {
+            this.setFilter('gaussianBlur');
+        },
+        gaussianBlurSize: 7,
+        medianBlur: function() {
+            this.setFilter('medianBlur');
+        },
+        medianBlurSize: 5,
+        bilateralFilter: function() {
+            this.setFilter('bilateralFilter');
+        },
+        bilateralFilterDiameter: 5,
+        bilateralFilterSigma: 75,
+        sobel: function() {
+            this.setFilter('sobel');
+        },
+        sobelSize: 3,
+        scharr: function() {
+            this.setFilter('scharr');
+        },
+        laplacian: function() {
+            this.setFilter('laplacian');
+        },
+        laplacianSize: 3,
+        canny: function() {
+            this.setFilter('canny');
+        },
+        cannyThreshold1: 150,
+        cannyThreshold2: 300,
+        cannyApertureSize: 3,
+        cannyL2Gradient: false,
+        contours: function() {
+            this.setFilter('contours');
+        },
+        contoursMode: cv.RETR_CCOMP,
+        contoursMethod: cv.CHAIN_APPROX_SIMPLE,
+        calcHist: function() {
+            this.setFilter('calcHist');
+        },
+        equalizeHist: function() {
+            this.setFilter('equalizeHist');
+        },
+        backprojection: function() {
+            this.setFilter('backprojection');
+        },
+        backprojectionRangeLow: 0,
+        backprojectionRangeHigh: 150,
+        morphology: function() {
+            this.setFilter('morphology');
+        },
+        morphologyShape: cv.MORPH_RECT,
+        morphologyOp: cv.MORPH_ERODE,
+        morphologySize: 5,
+        morphologyBorderType: cv.BORDER_CONSTANT,
+    };
+
+    let gui = new dat.GUI({autoPlace: false});
+    let guiContainer = document.getElementById('guiContainer');
+    guiContainer.appendChild(gui.domElement);
+
+    let lastFolder = null;
+    function closeLastFolder(folder) {
+        if (lastFolder != null && lastFolder != folder) {
+            lastFolder.close();
+        }
+        lastFolder = folder;
+    }
+
+    gui.add(controls, 'passThrough').name(filters['passThrough']).onChange(function() {
+        closeLastFolder(null);
+    });
+
+    let colorConversion = gui.addFolder('Color Conversion');
+    colorConversion.add(controls, 'gray').name(filters['gray']).onChange(function() {
+        closeLastFolder(null);
+    });
+
+    colorConversion.add(controls, 'hsv').name(filters['hsv']).onChange(function() {
+        closeLastFolder(null);
+    });
+
+    let inRange = colorConversion.addFolder(filters['inRange']);
+    inRange.domElement.onclick = function() {
+        closeLastFolder(inRange);
+        controls.inRange();
+    };
+    inRange.add(controls, 'inRangeLow', 0, 255, 1).name('lower boundary');
+    inRange.add(controls, 'inRangeHigh', 0, 255, 1).name('higher boundary');
+
+    // let geometricTransformations = gui.addFolder('Geometric Transformations');
+    // TODO
+
+    let thresholding = gui.addFolder('Thresholding');
+
+    let threshold = thresholding.addFolder(filters['threshold']);
+    threshold.domElement.onclick = function() {
+        closeLastFolder(threshold);
+        controls.threshold();
+    };
+    threshold.add(controls, 'thresholdValue', 0, 200, 1).name('threshold value');
+
+    let adaptiveThreshold = thresholding.addFolder(filters['adaptiveThreshold']);
+    adaptiveThreshold.domElement.onclick = function() {
+        closeLastFolder(adaptiveThreshold);
+        controls.adaptiveThreshold();
+    };
+    adaptiveThreshold.add(
+        controls, 'adaptiveBlockSize', 3, 99, 1).name('block size').onChange(
+        function(value) {
+            if (value % 2 === 0) controls.adaptiveBlockSize = value + 1;
+        });
+
+    let smoothing = gui.addFolder('Smoothing');
+
+    let gaussianBlur = smoothing.addFolder(filters['gaussianBlur']);
+    gaussianBlur.domElement.onclick = function() {
+        closeLastFolder(gaussianBlur);
+        controls.gaussianBlur();
+    };
+    gaussianBlur.add(
+        controls, 'gaussianBlurSize', 7, 99, 1).name('kernel size').onChange(
+        function(value) {
+            if (value % 2 === 0) controls.gaussianBlurSize = value + 1;
+        });
+
+    let medianBlur = smoothing.addFolder(filters['medianBlur']);
+    medianBlur.domElement.onclick = function() {
+        closeLastFolder(medianBlur);
+        controls.medianBlur();
+    };
+    medianBlur.add(
+        controls, 'medianBlurSize', 3, 99, 1).name('kernel size').onChange(
+        function(value) {
+            if (value % 2 === 0) controls.medianBlurSize = value + 1;
+        });
+
+    let bilateralFilter = smoothing.addFolder(filters['bilateralFilter']);
+    bilateralFilter.domElement.onclick = function() {
+        closeLastFolder(bilateralFilter);
+        controls.bilateralFilter();
+    };
+    bilateralFilter.add(controls, 'bilateralFilterDiameter', 1, 15, 1).name('diameter');
+    bilateralFilter.add(controls, 'bilateralFilterSigma', 1, 255, 1).name('sigma');
+
+    let morphology = gui.addFolder('Morphology');
+    morphology.domElement.onclick = function() {
+        closeLastFolder(morphology);
+        controls.morphology();
+    };
+    morphology.add(
+        controls, 'morphologyOp',
+        {'MORPH_ERODE': cv.MORPH_ERODE,
+         'MORPH_DILATE': cv.MORPH_DILATE,
+         'MORPH_OPEN ': cv.MORPH_OPEN,
+         'MORPH_CLOSE': cv.MORPH_CLOSE,
+         'MORPH_GRADIENT': cv.MORPH_GRADIENT,
+         'MORPH_TOPHAT': cv.MORPH_TOPHAT,
+         'MORPH_BLACKHAT': cv.MORPH_BLACKHAT}).name('operation');
+    morphology.add(
+        controls, 'morphologyShape',
+        {'MORPH_RECT': cv.MORPH_RECT,
+         'MORPH_CROSS': cv.MORPH_CROSS,
+         'MORPH_ELLIPSE': cv.MORPH_ELLIPSE}).name('shape');
+    morphology.add(
+        controls, 'morphologySize', 1, 15, 1).name('kernel size').onChange(
+        function(value) {
+            if (value % 2 === 0) controls.morphologySize = value + 1;
+        });
+    morphology.add(
+        controls, 'morphologyBorderType',
+        {'BORDER_CONSTANT': cv.BORDER_CONSTANT,
+         'BORDER_REPLICATE': cv.BORDER_REPLICATE,
+         'BORDER_REFLECT': cv.BORDER_REFLECT,
+         'BORDER_REFLECT_101': cv.BORDER_REFLECT_101}).name('boarder type');
+
+    let gradients = gui.addFolder('Gradients');
+    let sobel = gradients.addFolder(filters['sobel']);
+    sobel.domElement.onclick = function() {
+        closeLastFolder(sobel);
+        controls.sobel();
+    };
+    sobel.add(controls, 'sobelSize', 3, 19, 1).name('kernel size').onChange(function(value) {
+        if (value % 2 === 0) controls.sobelSize = value + 1;
+    });
+
+    gradients.add(controls, 'scharr').name(filters['scharr']).onChange(function() {
+        closeLastFolder(null);
+    });
+
+    let laplacian = gradients.addFolder(filters['laplacian']);
+    laplacian.domElement.onclick = function() {
+        closeLastFolder(laplacian);
+        controls.laplacian();
+    };
+    laplacian.add(
+        controls, 'laplacianSize', 1, 19, 1).name('kernel size').onChange(
+        function(value) {
+            if (value % 2 === 0) controls.laplacianSize = value + 1;
+        });
+
+    let canny = gui.addFolder(filters['canny']);
+    canny.domElement.onclick = function() {
+        closeLastFolder(canny);
+        controls.canny();
+    };
+    canny.add(controls, 'cannyThreshold1', 1, 500, 1).name('threshold1');
+    canny.add(controls, 'cannyThreshold2', 1, 500, 1).name('threshold2');
+    canny.add(controls, 'cannyApertureSize', 3, 7, 1).name('aperture size').onChange(
+        function(value) {
+            if (value % 2 === 0) controls.cannyApertureSize = value + 1;
+        });
+    canny.add(controls, 'cannyL2Gradient').name('l2 gradient');
+
+    let contours = gui.addFolder(filters['contours']);
+    contours.domElement.onclick = function() {
+        closeLastFolder(contours);
+        controls.contours();
+    };
+    contours.add(
+        controls, 'contoursMode',
+        {'RETR_EXTERNAL': cv.RETR_EXTERNAL,
+         'RETR_LIST': cv.RETR_LIST,
+         'RETR_CCOMP': cv.RETR_CCOMP,
+         'RETR_TREE': cv.RETR_TREE}).name('mode');
+    contours.add(
+        controls, 'contoursMethod',
+        {'CHAIN_APPROX_NONE': cv.CHAIN_APPROX_NONE,
+         'CHAIN_APPROX_SIMPLE': cv.CHAIN_APPROX_SIMPLE,
+         'CHAIN_APPROX_TC89_L1': cv.CHAIN_APPROX_TC89_L1,
+         'CHAIN_APPROX_TC89_KCOS': cv.CHAIN_APPROX_TC89_KCOS}).name('method');
+
+    let histograms = gui.addFolder('Histograms');
+    histograms.add(controls, 'calcHist').name(filters['calcHist']).onChange(function() {
+        closeLastFolder(null);
+    });
+    histograms.add(controls, 'equalizeHist').name(filters['equalizeHist']).onChange(function() {
+        closeLastFolder(null);
+    });
+
+    let backprojection = histograms.addFolder(filters['backprojection']);
+    backprojection.domElement.onclick = function() {
+        closeLastFolder(backprojection);
+        controls.backprojection();
+    };
+    backprojection.add(controls, 'backprojectionRangeLow', 0, 255, 1).name('range low');
+    backprojection.add(controls, 'backprojectionRangeHigh', 0, 255, 1).name('range high');
+}
+
+function startCamera() {
+    if (!streaming) {
+        utils.clearError();
+        utils.startCamera(resolution, onVideoStarted, 'videoInput');
+    } else {
+        utils.stopCamera();
+        onVideoStopped();
+    }
+}
+
+function onVideoStarted() {
+    height = video.videoHeight;
+    width = video.videoWidth;
+    video.setAttribute('width', width);
+    video.setAttribute('height', height);
+    streaming = true;
+    vc = new cv.VideoCapture(video);
+    startVideoProcessing();
+}
+
+function stopVideoProcessing() {
+    if (src != null && !src.isDeleted()) src.delete();
+    if (dstC1 != null && !dstC1.isDeleted()) dstC1.delete();
+    if (dstC3 != null && !dstC3.isDeleted()) dstC3.delete();
+    if (dstC4 != null && !dstC4.isDeleted()) dstC4.delete();
+}
+
+function onVideoStopped() {
+    if (!streaming) return;
+    stopVideoProcessing();
+    document.getElementById('canvasOutput').getContext('2d').clearRect(0, 0, width, height);
+    streaming = false;
+}
+
+utils.loadOpenCv(() => {
+    initUI();
+    startCamera();
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_intelligent_scissors.html

@@ -0,0 +1,127 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Intelligent Scissors Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Intelligent Scissors Example</h2>
+<p>
+    Click <b>Start</b> button to launch the code below.<br>
+    Then click on image to pick source point. After that you can hover mouse pointer over canvas to specify target point candidate.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more. You can choose another image (need to "Stop" first).
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Start</button> <button id="stopIt" disabled>Stop</button></div>
+<textarea class="code" rows="20" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div id="inputParams">
+  <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+  <canvas id="canvasInput"></canvas>
+</div>
+<div id="result" style="display:none">
+  <canvas id="canvasOutput"></canvas>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+//cv.resize(src, src, new cv.Size(1024, 1024));
+cv.imshow('canvasOutput', src);
+
+let tool = new cv.segmentation_IntelligentScissorsMB();
+tool.setEdgeFeatureCannyParameters(32, 100);
+tool.setGradientMagnitudeMaxLimit(200);
+tool.applyImage(src);
+
+let hasMap = false;
+
+let canvas = document.getElementById('canvasOutput');
+canvas.addEventListener('click', e => {
+    let startX = e.offsetX, startY = e.offsetY; console.log(startX, startY);
+    if (startX < src.cols && startY < src.rows)
+    {
+        console.time('buildMap');
+        tool.buildMap(new cv.Point(startX, startY));
+        console.timeEnd('buildMap');
+        hasMap = true;
+    }
+});
+canvas.addEventListener('mousemove', e => {
+    let x = e.offsetX, y = e.offsetY; //console.log(x, y);
+    let dst = src.clone();
+    if (hasMap && x >= 0 && x < src.cols && y >= 0 && y < src.rows)
+    {
+        let contour = new cv.Mat();
+        tool.getContour(new cv.Point(x, y), contour);
+        let contours = new cv.MatVector();
+        contours.push_back(contour);
+        let color = new cv.Scalar(0, 255, 0, 255);  // RGBA
+        cv.polylines(dst, contours, false, color, 1, cv.LINE_8);
+        contours.delete(); contour.delete();
+    }
+    cv.imshow('canvasOutput', dst);
+    dst.delete();
+});
+canvas.addEventListener('dispose', e => {
+    src.delete();
+    tool.delete();
+});
+</script>
+
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let disposeEvent = new Event('dispose');
+
+let tryIt = document.getElementById('tryIt');
+let stopIt = document.getElementById('stopIt');
+
+tryIt.addEventListener('click', () => {
+    let e_input = document.getElementById('inputParams');
+    e_input.style.display = 'none';
+
+    let e_result = document.getElementById("result")
+    e_result.style.display = '';
+
+    var e = document.getElementById("canvasOutput");
+    var e_new = e.cloneNode(true);
+    e.parentNode.replaceChild(e_new, e);  // reset event handlers
+
+    stopIt.removeAttribute('disabled');
+    tryIt.setAttribute('disabled', '');
+
+    utils.executeCode('codeEditor');
+});
+
+stopIt.addEventListener('click', () => {
+    let e_input = document.getElementById('inputParams');
+    e_input.style.display = '';
+
+    let e_result = document.getElementById("result")
+    e_result.style.display = 'none';
+
+    var e = document.getElementById("canvasOutput");
+    e.dispatchEvent(disposeEvent);
+
+    var e_new = e.cloneNode(true);
+    e.parentNode.replaceChild(e_new, e);  // reset event handlers
+
+    tryIt.removeAttribute('disabled');
+    stopIt.setAttribute('disabled', '');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_meanshift.html

@@ -0,0 +1,170 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>MeanShift Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>MeanShift Example</h2>
+<p>
+    Click <b>Start/Stop</b> button to start or stop the video.<br>
+    The <b>videoInput</b> is a &lt;video&gt; element used as meanShift input.
+    The <b>canvasOutput</b> is a &lt;canvas&gt; element used as meanShift output.<br>
+    The code of &lt;textarea&gt; will be executed when video is started.
+    You can modify the code to investigate more.
+</p>
+<div>
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<textarea class="code" rows="29" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+</div>
+<p class="err" id="errorMessage"></p>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <video id="videoInput" width="320" height="240" muted loop></video>
+        </td>
+        <td>
+            <canvas id="canvasOutput" width="320" height="240" ></canvas>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">videoInput</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    </table>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let video = document.getElementById('videoInput');
+let cap = new cv.VideoCapture(video);
+
+// take first frame of the video
+let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+cap.read(frame);
+
+// hardcode the initial location of window
+let trackWindow = new cv.Rect(150, 60, 63, 125);
+
+// set up the ROI for tracking
+let roi = frame.roi(trackWindow);
+let hsvRoi = new cv.Mat();
+cv.cvtColor(roi, hsvRoi, cv.COLOR_RGBA2RGB);
+cv.cvtColor(hsvRoi, hsvRoi, cv.COLOR_RGB2HSV);
+let mask = new cv.Mat();
+let lowScalar = new cv.Scalar(30, 30, 0);
+let highScalar = new cv.Scalar(180, 180, 180);
+let low = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), lowScalar);
+let high = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), highScalar);
+cv.inRange(hsvRoi, low, high, mask);
+let roiHist = new cv.Mat();
+let hsvRoiVec = new cv.MatVector();
+hsvRoiVec.push_back(hsvRoi);
+cv.calcHist(hsvRoiVec, [0], mask, roiHist, [180], [0, 180]);
+cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX);
+
+// delete useless mats.
+roi.delete(); hsvRoi.delete(); mask.delete(); low.delete(); high.delete(); hsvRoiVec.delete();
+
+// Setup the termination criteria, either 10 iteration or move by at least 1 pt
+let termCrit = new cv.TermCriteria(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1);
+
+let hsv = new cv.Mat(video.height, video.width, cv.CV_8UC3);
+let dst = new cv.Mat();
+let hsvVec = new cv.MatVector();
+hsvVec.push_back(hsv);
+
+const FPS = 30;
+function processVideo() {
+    try {
+        if (!streaming) {
+            // clean and stop.
+            frame.delete(); dst.delete(); hsvVec.delete(); roiHist.delete(); hsv.delete();
+            return;
+        }
+        let begin = Date.now();
+
+        // start processing.
+        cap.read(frame);
+        cv.cvtColor(frame, hsv, cv.COLOR_RGBA2RGB);
+        cv.cvtColor(hsv, hsv, cv.COLOR_RGB2HSV);
+        cv.calcBackProject(hsvVec, [0], roiHist, dst, [0, 180], 1);
+
+        // Apply meanshift to get the new location
+        // and it also returns number of iterations meanShift took to converge,
+        // which is useless in this demo.
+        [, trackWindow] = cv.meanShift(dst, trackWindow, termCrit);
+
+        // Draw it on image
+        let [x, y, w, h] = [trackWindow.x, trackWindow.y, trackWindow.width, trackWindow.height];
+        cv.rectangle(frame, new cv.Point(x, y), new cv.Point(x+w, y+h), [255, 0, 0, 255], 2);
+        cv.imshow('canvasOutput', frame);
+
+        // schedule the next one.
+        let delay = 1000/FPS - (Date.now() - begin);
+        setTimeout(processVideo, delay);
+    } catch (err) {
+        utils.printError(err);
+    }
+};
+
+// schedule the first one.
+setTimeout(processVideo, 0);
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+
+let streaming = false;
+let videoInput = document.getElementById('videoInput');
+let startAndStop = document.getElementById('startAndStop');
+let canvasOutput = document.getElementById('canvasOutput');
+let canvasContext = canvasOutput.getContext('2d');
+
+startAndStop.addEventListener('click', () => {
+    if (!streaming) {
+        utils.clearError();
+        videoInput.play().then(() => {
+            onVideoStarted();
+        });
+    } else {
+        videoInput.pause();
+        videoInput.currentTime = 0;
+        onVideoStopped();
+    }
+});
+
+function onVideoStarted() {
+    streaming = true;
+    startAndStop.innerText = 'Stop';
+    videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
+    utils.executeCode('codeEditor');
+}
+
+function onVideoStopped() {
+    streaming = false;
+    canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
+    startAndStop.innerText = 'Start';
+}
+
+utils.loadOpenCv(() => {
+    videoInput.addEventListener('canplay', () => {
+        startAndStop.removeAttribute('disabled');
+    });
+    videoInput.src = 'cup.mp4';
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_blackHat.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Black Hat Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Black Hat Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2RGB);
+let dst = new cv.Mat();
+let M = cv.Mat.ones(53, 53, cv.CV_8U);
+// You can try more different parameters
+cv.morphologyEx(src, dst, cv.MORPH_BLACKHAT, M);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_closing.html

@@ -0,0 +1,68 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Closing Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Closing Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let M = cv.Mat.ones(5, 5, cv.CV_8U);
+// You can try more different parameters
+cv.morphologyEx(src, dst, cv.MORPH_CLOSE, M);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_dilate.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Dilate Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Dilate Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let M = cv.Mat.ones(5, 5, cv.CV_8U);
+let anchor = new cv.Point(-1, -1);
+// You can try more different parameters
+cv.dilate(src, dst, M, anchor, 1, cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_erode.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Erode Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Erode Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let M = cv.Mat.ones(5, 5, cv.CV_8U);
+let anchor = new cv.Point(-1, -1);
+// You can try more different parameters
+cv.erode(src, dst, M, anchor, 1, cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_getStructuringElement.html

@@ -0,0 +1,71 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Get Structuring Element Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Get Structuring Element Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2RGB);
+let dst = new cv.Mat();
+let M = new cv.Mat();
+let ksize = new cv.Size(5, 5);
+// You can try more different parameters
+M = cv.getStructuringElement(cv.MORPH_CROSS, ksize);
+cv.morphologyEx(src, dst, cv.MORPH_GRADIENT, M);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_gradient.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Gradient Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Gradient Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2RGB);
+let dst = new cv.Mat();
+let M = cv.Mat.ones(5, 5, cv.CV_8U);
+// You can try more different parameters
+cv.morphologyEx(src, dst, cv.MORPH_GRADIENT, M);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_opening.html

@@ -0,0 +1,70 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image Opening Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image Opening Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+let M = cv.Mat.ones(5, 5, cv.CV_8U);
+let anchor = new cv.Point(-1, -1);
+// You can try more different parameters
+cv.morphologyEx(src, dst, cv.MORPH_OPEN, M, anchor, 1,
+                cv.BORDER_CONSTANT, cv.morphologyDefaultBorderValue());
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_morphological_ops_topHat.html

@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Top Hat Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Top Hat Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+cv.cvtColor(src, src, cv.COLOR_RGBA2RGB);
+let dst = new cv.Mat();
+let M = cv.Mat.ones(9, 9, cv.CV_8U);
+// You can try more different parameters
+cv.morphologyEx(src, dst, cv.MORPH_TOPHAT, M);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete(); M.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('shape.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_object_detection.html

@@ -0,0 +1,387 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Object Detection Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Object Detection Example</h2>
+<p>
+    This tutorial shows you how to write an object detection example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including get boxes from output and draw boxes into the image.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [300, 300];
+mean = [127.5, 127.5, 127.5];
+std = 0.007843;
+swapRB = false;
+confThreshold = 0.5;
+nmsThreshold = 0.4;
+
+// The type of output, can be YOLO or SSD
+outType = "SSD";
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/object_detection_classes_pascal_voc.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result, labels);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+postProcess = function(result, labels) {
+    let canvasOutput = document.getElementById('canvasOutput');
+    const outputWidth = canvasOutput.width;
+    const outputHeight = canvasOutput.height;
+    const resultData = result.data32F;
+
+    // Get the boxes(with class and confidence) from the output
+    let boxes = [];
+    switch(outType) {
+        case "YOLO": {
+            const vecNum = result.matSize[0];
+            const vecLength = result.matSize[1];
+            const classNum = vecLength - 5;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let scores = vector.slice(5, vecLength);
+                let classId = scores.indexOf(Math.max(...scores));
+                let confidence = scores[classId];
+                if (confidence > confThreshold) {
+                    let center_x = Math.round(vector[0] * outputWidth);
+                    let center_y = Math.round(vector[1] * outputHeight);
+                    let width = Math.round(vector[2] * outputWidth);
+                    let height = Math.round(vector[3] * outputHeight);
+                    let left = Math.round(center_x - width / 2);
+                    let top = Math.round(center_y - height / 2);
+
+                    let box = {
+                        scores: scores,
+                        classId: classId,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+
+            // NMS(Non Maximum Suppression) algorithm
+            let boxNum = boxes.length;
+            let tmp_boxes = [];
+            let sorted_boxes = [];
+            for (let c = 0; c < classNum; ++c) {
+                for (let i = 0; i < boxes.length; ++i) {
+                    tmp_boxes[i] = [boxes[i], i];
+                }
+                sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
+                for (let i = 0; i < boxNum; ++i) {
+                    if (sorted_boxes[i][0].scores[c] === 0) continue;
+                    else {
+                        for (let j = i + 1; j < boxNum; ++j) {
+                            if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
+                                boxes[sorted_boxes[j][1]].toDraw = false;
+                            }
+                        }
+                    }
+                }
+            }
+        } break;
+        case "SSD": {
+            const vecNum = result.matSize[2];
+            const vecLength = 7;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let confidence = vector[2];
+                if (confidence > confThreshold) {
+                    let left, top, right, bottom, width, height;
+                    left = Math.round(vector[3]);
+                    top = Math.round(vector[4]);
+                    right = Math.round(vector[5]);
+                    bottom = Math.round(vector[6]);
+                    width = right - left + 1;
+                    height = bottom - top + 1;
+                    if (width <= 2 || height <= 2) {
+                        left = Math.round(vector[3] * outputWidth);
+                        top = Math.round(vector[4] * outputHeight);
+                        right = Math.round(vector[5] * outputWidth);
+                        bottom = Math.round(vector[6] * outputHeight);
+                        width = right - left + 1;
+                        height = bottom - top + 1;
+                    }
+                    let box = {
+                        classId: vector[1] - 1,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+        } break;
+        default:
+            console.error(`Unsupported output type ${outType}`)
+    }
+
+    // Draw the saved box into the image
+    let image = cv.imread("canvasInput");
+    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
+    cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
+    let boxNum = boxes.length;
+    for (let i = 0; i < boxNum; ++i) {
+        if (boxes[i].toDraw) {
+            drawBox(boxes[i]);
+        }
+    }
+
+    return output;
+
+
+    // Calculate the IOU(Intersection over Union) of two boxes
+    function IOU(box1, box2) {
+        let bounding1 = box1.bounding;
+        let bounding2 = box2.bounding;
+        let s1 = bounding1[2] * bounding1[3];
+        let s2 = bounding2[2] * bounding2[3];
+
+        let left1 = bounding1[0];
+        let right1 = left1 + bounding1[2];
+        let left2 = bounding2[0];
+        let right2 = left2 + bounding2[2];
+        let overlapW = calOverlap([left1, right1], [left2, right2]);
+
+        let top1 = bounding2[1];
+        let bottom1 = top1 + bounding1[3];
+        let top2 = bounding2[1];
+        let bottom2 = top2 + bounding2[3];
+        let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
+
+        let overlapS = overlapW * overlapH;
+        return overlapS / (s1 + s2 + overlapS);
+    }
+
+    // Calculate the overlap range of two vector
+    function calOverlap(range1, range2) {
+        let min1 = range1[0];
+        let max1 = range1[1];
+        let min2 = range2[0];
+        let max2 = range2[1];
+
+        if (min2 > min1 && min2 < max1) {
+            return max1 - min2;
+        } else if (max2 > min1 && max2 < max1) {
+            return max2 - min1;
+        } else {
+            return 0;
+        }
+    }
+
+    // Draw one predict box into the origin image
+    function drawBox(box) {
+        let bounding = box.bounding;
+        let left = bounding[0];
+        let top = bounding[1];
+        let width = bounding[2];
+        let height = bounding[3];
+
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
+                             new cv.Scalar(0, 255, 0));
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
+                             new cv.Scalar(255, 255, 255), cv.FILLED);
+        let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
+        cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
+                                 new cv.Scalar(0, 0, 0));
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_object_detection_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'lena.png';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var postProcess = function(result, labels) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            cv.imshow('canvasOutput', output);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_object_detection_model_info.json

@@ -0,0 +1,39 @@
+{
+    "caffe": [
+        {
+            "model": "mobilenet_SSD",
+            "inputSize": "300, 300",
+            "mean": "127.5, 127.5, 127.5",
+            "std": "0.007843",
+            "swapRB": "false",
+            "outType": "SSD",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/object_detection_classes_pascal_voc.txt",
+            "modelUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/mobilenet_iter_73000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/deploy.prototxt"
+        },
+        {
+            "model": "VGG_SSD",
+            "inputSize": "300, 300",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "outType": "SSD",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/object_detection_classes_pascal_voc.txt",
+            "modelUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download",
+            "configUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download"
+        }
+    ],
+    "darknet": [
+        {
+            "model": "yolov2_tiny",
+            "inputSize": "416, 416",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "outType": "YOLO",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/object_detection_classes_yolov3.txt",
+            "modelUrl": "https://pjreddie.com/media/files/yolov2-tiny.weights",
+            "configUrl": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg"
+        }
+    ]
+}

OpenCV/opencv/doc/js_tutorials/js_assets/js_object_detection_with_camera.html

@@ -0,0 +1,402 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Object Detection Example with Camera</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Object Detection Example with Camera </h2>
+<p>
+    This tutorial shows you how to write an object detection example with camera.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
+</p>
+
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <video id="videoInput" width="400" height="400"></video>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    videoInput
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
+    <textarea class="code" rows="34" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including get boxes from output and draw boxes into the image.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [300, 300];
+mean = [127.5, 127.5, 127.5];
+std = 0.007843;
+swapRB = false;
+confThreshold = 0.5;
+nmsThreshold = 0.4;
+
+// the type of output, can be YOLO or SSD
+outType = "SSD";
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/dnn/object_detection_classes_pascal_voc.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+let frame = new cv.Mat(videoInput.height, videoInput.width, cv.CV_8UC4);
+let cap = new cv.VideoCapture(videoInput);
+
+main = async function(frame) {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result, labels, frame);
+
+    updateResult(output, time);
+    setTimeout(processVideo, 0);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+
+function processVideo() {
+    try {
+        if (!streaming) {
+            return;
+        }
+        cap.read(frame);
+        main(frame);
+    } catch (err) {
+        utils.printError(err);
+    }
+}
+
+setTimeout(processVideo, 0);
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+postProcess = function(result, labels, frame) {
+    let canvasOutput = document.getElementById('canvasOutput');
+    const outputWidth = canvasOutput.width;
+    const outputHeight = canvasOutput.height;
+    const resultData = result.data32F;
+
+    // Get the boxes(with class and confidence) from the output
+    let boxes = [];
+    switch(outType) {
+        case "YOLO": {
+            const vecNum = result.matSize[0];
+            const vecLength = result.matSize[1];
+            const classNum = vecLength - 5;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let scores = vector.slice(5, vecLength);
+                let classId = scores.indexOf(Math.max(...scores));
+                let confidence = scores[classId];
+                if (confidence > confThreshold) {
+                    let center_x = Math.round(vector[0] * outputWidth);
+                    let center_y = Math.round(vector[1] * outputHeight);
+                    let width = Math.round(vector[2] * outputWidth);
+                    let height = Math.round(vector[3] * outputHeight);
+                    let left = Math.round(center_x - width / 2);
+                    let top = Math.round(center_y - height / 2);
+
+                    let box = {
+                        scores: scores,
+                        classId: classId,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+
+            // NMS(Non Maximum Suppression) algorithm
+            let boxNum = boxes.length;
+            let tmp_boxes = [];
+            let sorted_boxes = [];
+            for (let c = 0; c < classNum; ++c) {
+                for (let i = 0; i < boxes.length; ++i) {
+                    tmp_boxes[i] = [boxes[i], i];
+                }
+                sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
+                for (let i = 0; i < boxNum; ++i) {
+                    if (sorted_boxes[i][0].scores[c] === 0) continue;
+                    else {
+                        for (let j = i + 1; j < boxNum; ++j) {
+                            if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
+                                boxes[sorted_boxes[j][1]].toDraw = false;
+                            }
+                        }
+                    }
+                }
+            }
+        } break;
+        case "SSD": {
+            const vecNum = result.matSize[2];
+            const vecLength = 7;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let confidence = vector[2];
+                if (confidence > confThreshold) {
+                    let left, top, right, bottom, width, height;
+                    left = Math.round(vector[3]);
+                    top = Math.round(vector[4]);
+                    right = Math.round(vector[5]);
+                    bottom = Math.round(vector[6]);
+                    width = right - left + 1;
+                    height = bottom - top + 1;
+                    if (width <= 2 || height <= 2) {
+                        left = Math.round(vector[3] * outputWidth);
+                        top = Math.round(vector[4] * outputHeight);
+                        right = Math.round(vector[5] * outputWidth);
+                        bottom = Math.round(vector[6] * outputHeight);
+                        width = right - left + 1;
+                        height = bottom - top + 1;
+                    }
+                    let box = {
+                        classId: vector[1] - 1,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+        } break;
+        default:
+            console.error(`Unsupported output type ${outType}`)
+    }
+
+    // Draw the saved box into the image
+    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
+    cv.cvtColor(frame, output, cv.COLOR_RGBA2RGB);
+    let boxNum = boxes.length;
+    for (let i = 0; i < boxNum; ++i) {
+        if (boxes[i].toDraw) {
+            drawBox(boxes[i]);
+        }
+    }
+
+    return output;
+
+
+    // Calculate the IOU(Intersection over Union) of two boxes
+    function IOU(box1, box2) {
+        let bounding1 = box1.bounding;
+        let bounding2 = box2.bounding;
+        let s1 = bounding1[2] * bounding1[3];
+        let s2 = bounding2[2] * bounding2[3];
+
+        let left1 = bounding1[0];
+        let right1 = left1 + bounding1[2];
+        let left2 = bounding2[0];
+        let right2 = left2 + bounding2[2];
+        let overlapW = calOverlap([left1, right1], [left2, right2]);
+
+        let top1 = bounding2[1];
+        let bottom1 = top1 + bounding1[3];
+        let top2 = bounding2[1];
+        let bottom2 = top2 + bounding2[3];
+        let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
+
+        let overlapS = overlapW * overlapH;
+        return overlapS / (s1 + s2 + overlapS);
+    }
+
+    // Calculate the overlap range of two vector
+    function calOverlap(range1, range2) {
+        let min1 = range1[0];
+        let max1 = range1[1];
+        let min2 = range2[0];
+        let max2 = range2[1];
+
+        if (min2 > min1 && min2 < max1) {
+            return max1 - min2;
+        } else if (max2 > min1 && max2 < max1) {
+            return max2 - min1;
+        } else {
+            return 0;
+        }
+    }
+
+    // Draw one predict box into the origin image
+    function drawBox(box) {
+        let bounding = box.bounding;
+        let left = bounding[0];
+        let top = bounding[1];
+        let width = bounding[2];
+        let height = bounding[3];
+
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
+                             new cv.Scalar(0, 255, 0));
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
+                             new cv.Scalar(255, 255, 255), cv.FILLED);
+        let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
+        cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
+                                 new cv.Scalar(0, 0, 0));
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_object_detection_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+
+    let videoInput = document.getElementById('videoInput');
+    let streaming = false;
+    let startAndStop = document.getElementById('startAndStop');
+    startAndStop.addEventListener('click', () => {
+        if (!streaming) {
+            utils.clearError();
+            utils.startCamera('qvga', onVideoStarted, 'videoInput');
+        } else {
+            utils.stopCamera();
+            onVideoStopped();
+        }
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        startAndStop.removeAttribute('disabled');
+    });
+
+    var main = async function(frame) {};
+    var postProcess = function(result, labels, frame) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function onVideoStarted() {
+        streaming = true;
+        startAndStop.innerText = 'Stop';
+        videoInput.width = videoInput.videoWidth;
+        videoInput.height = videoInput.videoHeight;
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+    }
+
+    function onVideoStopped() {
+        streaming = false;
+        startAndStop.innerText = 'Start';
+        initStatus();
+    }
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            cv.imshow('canvasOutput', output);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_optical_flow_dense.html

@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Dense Optical Flow Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Dense Optical Flow Example</h2>
+<p>
+    Click <b>Start/Stop</b> button to start or stop the video.<br>
+    The <b>videoInput</b> is a &lt;video&gt; element used as input.
+    The <b>canvasOutput</b> is a &lt;canvas&gt; element used as output.<br>
+    We get a 2-channel array with optical flow vectors, (u,v). We find their magnitude and direction.
+    We color code the result for better visualization. Direction corresponds to Hue value of the image.
+    Magnitude corresponds to Value plane.<br>
+    The code of &lt;textarea&gt; will be executed when video is started.<br>
+    You can modify the code to investigate more.
+</p>
+<div>
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<textarea class="code" rows="29" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+</div>
+<p class="err" id="errorMessage"></p>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <video id="videoInput" width="320" height="240" muted></video>
+        </td>
+        <td>
+            <canvas id="canvasOutput" width="320" height="240" ></canvas>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">videoInput</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    </table>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let video = document.getElementById('videoInput');
+let cap = new cv.VideoCapture(video);
+
+// take first frame of the video
+let frame1 = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+cap.read(frame1);
+
+let prvs = new cv.Mat();
+cv.cvtColor(frame1, prvs, cv.COLOR_RGBA2GRAY);
+frame1.delete();
+let hsv = new cv.Mat();
+let hsv0 = new cv.Mat(video.height, video.width, cv.CV_8UC1);
+let hsv1 = new cv.Mat(video.height, video.width, cv.CV_8UC1, new cv.Scalar(255));
+let hsv2 = new cv.Mat(video.height, video.width, cv.CV_8UC1);
+let hsvVec = new cv.MatVector();
+hsvVec.push_back(hsv0); hsvVec.push_back(hsv1); hsvVec.push_back(hsv2);
+
+let frame2 = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let next = new cv.Mat(video.height, video.width, cv.CV_8UC1);
+let flow = new cv.Mat(video.height, video.width, cv.CV_32FC2);
+let flowVec = new cv.MatVector();
+let mag = new cv.Mat(video.height, video.width, cv.CV_32FC1);
+let ang = new cv.Mat(video.height, video.width, cv.CV_32FC1);
+let rgb = new cv.Mat(video.height, video.width, cv.CV_8UC3);
+
+const FPS = 30;
+function processVideo() {
+    try {
+        if (!streaming) {
+            // clean and stop.
+            prvs.delete(); hsv.delete(); hsv0.delete(); hsv1.delete(); hsv2.delete();
+            hsvVec.delete(); frame2.delete(); flow.delete(); flowVec.delete(); next.delete();
+            mag.delete(); ang.delete(); rgb.delete();
+            return;
+        }
+        let begin = Date.now();
+
+        // start processing.
+        cap.read(frame2);
+        cv.cvtColor(frame2, next, cv.COLOR_RGBA2GRAY);
+        cv.calcOpticalFlowFarneback(prvs, next, flow, 0.5, 3, 15, 3, 5, 1.2, 0);
+        cv.split(flow, flowVec);
+        let u = flowVec.get(0);
+        let v = flowVec.get(1);
+        cv.cartToPolar(u, v, mag, ang);
+        u.delete(); v.delete();
+        ang.convertTo(hsv0, cv.CV_8UC1, 180/Math.PI/2);
+        cv.normalize(mag, hsv2, 0, 255, cv.NORM_MINMAX, cv.CV_8UC1);
+        cv.merge(hsvVec, hsv);
+        cv.cvtColor(hsv, rgb, cv.COLOR_HSV2RGB);
+        cv.imshow('canvasOutput', rgb);
+        next.copyTo(prvs);
+
+        // schedule the next one.
+        let delay = 1000/FPS - (Date.now() - begin);
+        setTimeout(processVideo, delay);
+    } catch (err) {
+        utils.printError(err);
+    }
+};
+
+// schedule the first one.
+setTimeout(processVideo, 0);
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+
+let streaming = false;
+let videoInput = document.getElementById('videoInput');
+let startAndStop = document.getElementById('startAndStop');
+
+startAndStop.addEventListener('click', () => {
+    if (!streaming) {
+        utils.clearError();
+        videoInput.play().then(() => {
+            onVideoStarted();
+        });
+    } else {
+        videoInput.pause();
+        videoInput.currentTime = 0;
+        onVideoStopped();
+    }
+});
+
+function onVideoStarted() {
+    streaming = true;
+    startAndStop.innerText = 'Stop';
+    videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
+    utils.executeCode('codeEditor');
+}
+
+function onVideoStopped() {
+    streaming = false;
+    startAndStop.innerText = 'Start';
+}
+
+videoInput.addEventListener('ended', () => {
+    onVideoStopped();
+});
+
+utils.loadOpenCv(() => {
+    videoInput.addEventListener('canplay', () => {
+        startAndStop.removeAttribute('disabled');
+    });
+    videoInput.src = 'box.mp4';
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_optical_flow_lucas_kanade.html

@@ -0,0 +1,190 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Lucas-Kanade Optical Flow Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Lucas-Kanade Optical Flow Example</h2>
+<p>
+    Click <b>Start/Stop</b> button to start or stop the video.<br>
+    The <b>videoInput</b> is a &lt;video&gt; element used as input.
+    The <b>canvasOutput</b> is a &lt;canvas&gt; element used as output.<br>
+    To decide the points, we use <b>cv.goodFeaturesToTrack()</b>. We take the first frame, detect some Shi-Tomasi corner points in it, then we iteratively track those points using <b>cv.calcOpticalFlowPyrLK</b>.<br>
+    The code of &lt;textarea&gt; will be executed when video is started.<br>
+    You can modify the code to investigate more.
+</p>
+<div>
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<textarea class="code" rows="29" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+</div>
+<p class="err" id="errorMessage"></p>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <video id="videoInput" width="320" height="240" muted></video>
+        </td>
+        <td>
+            <canvas id="canvasOutput" width="320" height="240" ></canvas>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">videoInput</div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+        <td></td>
+        <td></td>
+    </tr>
+    </table>
+</div>
+<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let video = document.getElementById('videoInput');
+let cap = new cv.VideoCapture(video);
+
+// parameters for ShiTomasi corner detection
+let [maxCorners, qualityLevel, minDistance, blockSize] = [30, 0.3, 7, 7];
+
+// parameters for lucas kanade optical flow
+let winSize = new cv.Size(15, 15);
+let maxLevel = 2;
+let criteria = new cv.TermCriteria(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03);
+
+// create some random colors
+let color = [];
+for (let i = 0; i < maxCorners; i++) {
+    color.push(new cv.Scalar(parseInt(Math.random()*255), parseInt(Math.random()*255),
+                             parseInt(Math.random()*255), 255));
+}
+
+// take first frame and find corners in it
+let oldFrame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+cap.read(oldFrame);
+let oldGray = new cv.Mat();
+cv.cvtColor(oldFrame, oldGray, cv.COLOR_RGB2GRAY);
+let p0 = new cv.Mat();
+let none = new cv.Mat();
+cv.goodFeaturesToTrack(oldGray, p0, maxCorners, qualityLevel, minDistance, none, blockSize);
+
+// Create a mask image for drawing purposes
+let zeroEle = new cv.Scalar(0, 0, 0, 255);
+let mask = new cv.Mat(oldFrame.rows, oldFrame.cols, oldFrame.type(), zeroEle);
+
+let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let frameGray = new cv.Mat();
+let p1 = new cv.Mat();
+let st = new cv.Mat();
+let err = new cv.Mat();
+
+const FPS = 30;
+function processVideo() {
+    try {
+        if (!streaming) {
+            // clean and stop.
+            frame.delete(); oldGray.delete(); p0.delete(); p1.delete(); err.delete(); mask.delete();
+            return;
+        }
+        let begin = Date.now();
+
+        // start processing.
+        cap.read(frame);
+        cv.cvtColor(frame, frameGray, cv.COLOR_RGBA2GRAY);
+
+        // calculate optical flow
+        cv.calcOpticalFlowPyrLK(oldGray, frameGray, p0, p1, st, err, winSize, maxLevel, criteria);
+
+        // select good points
+        let goodNew = [];
+        let goodOld = [];
+        for (let i = 0; i < st.rows; i++) {
+            if (st.data[i] === 1) {
+                goodNew.push(new cv.Point(p1.data32F[i*2], p1.data32F[i*2+1]));
+                goodOld.push(new cv.Point(p0.data32F[i*2], p0.data32F[i*2+1]));
+            }
+        }
+
+        // draw the tracks
+        for (let i = 0; i < goodNew.length; i++) {
+            cv.line(mask, goodNew[i], goodOld[i], color[i], 2);
+            cv.circle(frame, goodNew[i], 5, color[i], -1);
+        }
+        cv.add(frame, mask, frame);
+
+        cv.imshow('canvasOutput', frame);
+
+        // now update the previous frame and previous points
+        frameGray.copyTo(oldGray);
+        p0.delete(); p0 = null;
+        p0 = new cv.Mat(goodNew.length, 1, cv.CV_32FC2);
+        for (let i = 0; i < goodNew.length; i++) {
+            p0.data32F[i*2] = goodNew[i].x;
+            p0.data32F[i*2+1] = goodNew[i].y;
+        }
+
+        // schedule the next one.
+        let delay = 1000/FPS - (Date.now() - begin);
+        setTimeout(processVideo, delay);
+    } catch (err) {
+        utils.printError(err);
+    }
+};
+
+// schedule the first one.
+setTimeout(processVideo, 0);
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+
+let streaming = false;
+let videoInput = document.getElementById('videoInput');
+let startAndStop = document.getElementById('startAndStop');
+
+startAndStop.addEventListener('click', () => {
+    if (!streaming) {
+        utils.clearError();
+        videoInput.play().then(() => {
+            onVideoStarted();
+        });
+    } else {
+        videoInput.pause();
+        videoInput.currentTime = 0;
+        onVideoStopped();
+    }
+});
+
+function onVideoStarted() {
+    streaming = true;
+    startAndStop.innerText = 'Stop';
+    videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
+    utils.executeCode('codeEditor');
+}
+
+function onVideoStopped() {
+    streaming = false;
+    startAndStop.innerText = 'Start';
+}
+
+videoInput.addEventListener('ended', () => {
+    onVideoStopped();
+});
+
+utils.loadOpenCv(() => {
+    videoInput.addEventListener('canplay', () => {
+        startAndStop.removeAttribute('disabled');
+    });
+    videoInput.src = 'box.mp4';
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_pose_estimation.html

@@ -0,0 +1,327 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Pose Estimation Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Pose Estimation Example</h2>
+<p>
+    This tutorial shows you how to write an pose estimation example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="250"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="250"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.The pairs of keypoints of different dataset.</p>
+    <textarea class="code" rows="30" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including get the predicted points and draw lines into the image.</p>
+    <textarea class="code" rows="30" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [368, 368];
+mean = [0, 0, 0];
+std = 0.00392;
+swapRB = false;
+threshold = 0.1;
+
+// the pairs of keypoint, can be "COCO", "MPI" and "BODY_25"
+dataset = "COCO";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet4" type="text/code-snippet">
+BODY_PARTS = {};
+POSE_PAIRS = [];
+
+if (dataset === 'COCO') {
+    BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
+                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
+                   "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
+                   "LEye": 15, "REar": 16, "LEar": 17, "Background": 18 };
+
+    POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
+                   ["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
+                   ["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
+                   ["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
+                   ["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
+} else if (dataset === 'MPI') {
+    BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
+                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
+                   "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
+                   "Background": 15 }
+
+    POSE_PAIRS = [ ["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"],
+                   ["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
+                   ["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
+                   ["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
+} else if (dataset === 'BODY_25') {
+    BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
+                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "MidHip": 8, "RHip": 9,
+                   "RKnee": 10, "RAnkle": 11, "LHip": 12, "LKnee": 13, "LAnkle": 14,
+                   "REye": 15, "LEye": 16, "REar": 17, "LEar": 18, "LBigToe": 19,
+                   "LSmallToe": 20, "LHeel": 21, "RBigToe": 22, "RSmallToe": 23,
+                   "RHeel": 24, "Background": 25 }
+
+    POSE_PAIRS = [ ["Neck", "Nose"], ["Neck", "RShoulder"],
+                   ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
+                   ["RElbow", "RWrist"], ["LShoulder", "LElbow"],
+                   ["LElbow", "LWrist"], ["Nose", "REye"],
+                   ["REye", "REar"], ["Nose", "LEye"],
+                   ["LEye", "LEar"], ["Neck", "MidHip"],
+                   ["MidHip", "RHip"], ["RHip", "RKnee"],
+                   ["RKnee", "RAnkle"], ["RAnkle", "RBigToe"],
+                   ["RBigToe", "RSmallToe"], ["RAnkle", "RHeel"],
+                   ["MidHip", "LHip"], ["LHip", "LKnee"],
+                   ["LKnee", "LAnkle"], ["LAnkle", "LBigToe"],
+                   ["LBigToe", "LSmallToe"], ["LAnkle", "LHeel"] ]
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+postProcess = function(result) {
+    const resultData = result.data32F;
+    const matSize = result.matSize;
+    const size1 = matSize[1];
+    const size2 = matSize[2];
+    const size3 = matSize[3];
+    const mapSize = size2 * size3;
+
+    let canvasOutput = document.getElementById('canvasOutput');
+    const outputWidth = canvasOutput.width;
+    const outputHeight = canvasOutput.height;
+
+    let image = cv.imread("canvasInput");
+    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
+    cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
+
+    // get position of keypoints from output
+    let points = [];
+    for (let i = 0; i < Object.keys(BODY_PARTS).length; ++i) {
+        heatMap = resultData.slice(i*mapSize, (i+1)*mapSize);
+
+        let maxIndex = 0;
+        let maxConf = heatMap[0];
+        for (index in heatMap) {
+            if (heatMap[index] > heatMap[maxIndex]) {
+                maxIndex = index;
+                maxConf = heatMap[index];
+            }
+        }
+
+        if (maxConf > threshold) {
+            indexX = maxIndex % size3;
+            indexY = maxIndex / size3;
+
+            x = outputWidth * indexX / size3;
+            y = outputHeight * indexY / size2;
+
+            points[i] = [Math.round(x), Math.round(y)];
+        }
+    }
+
+    // draw the points and lines into the image
+    for (pair of POSE_PAIRS) {
+        partFrom = pair[0];
+        partTo = pair[1];
+        idFrom = BODY_PARTS[partFrom];
+        idTo = BODY_PARTS[partTo];
+        pointFrom = points[idFrom];
+        pointTo = points[idTo];
+
+        if (points[idFrom] && points[idTo]) {
+            cv.line(output, new cv.Point(pointFrom[0], pointFrom[1]),
+                            new cv.Point(pointTo[0], pointTo[1]), new cv.Scalar(0, 255, 0), 3);
+            cv.ellipse(output, new cv.Point(pointFrom[0], pointFrom[1]), new cv.Size(3, 3), 0, 0, 360,
+                               new cv.Scalar(0, 0, 255), cv.FILLED);
+            cv.ellipse(output, new cv.Point(pointTo[0], pointTo[1]), new cv.Size(3, 3), 0, 0, 360,
+                               new cv.Scalar(0, 0, 255), cv.FILLED);
+        }
+    }
+
+    return output;
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_pose_estimation_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor2').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor3').value = loadModelCode;
+
+    utils.loadCode('codeSnippet4', 'codeEditor4');
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'roi.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var postProcess = function(result) {};
+
+    utils.executeCode('codeEditor');
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
+            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
+            cv.imshow('canvasOutput', resized);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_pose_estimation_model_info.json

@@ -0,0 +1,34 @@
+{
+    "caffe": [
+        {
+            "model": "body_25",
+            "inputSize": "368, 368",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "dataset": "BODY_25",
+            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/body_25/pose_deploy.prototxt"
+        },
+        {
+            "model": "coco",
+            "inputSize": "368, 368",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "dataset": "COCO",
+            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/coco/pose_deploy_linevec.prototxt"
+        },
+        {
+            "model": "mpi",
+            "inputSize": "368, 368",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "dataset": "MPI",
+            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/mpi/pose_deploy_linevec.prototxt"
+        }
+    ]
+}

OpenCV/opencv/doc/js_tutorials/js_assets/js_pyramids_pyrDown.html

@@ -0,0 +1,67 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image PyrDown Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image PyrDown Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// You can try more different parameters
+cv.pyrDown(src, dst, new cv.Size(0, 0), cv.BORDER_DEFAULT);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_pyramids_pyrUp.html

@@ -0,0 +1,67 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<title>Image PyrUp Example</title>
+<link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+<body>
+<h2>Image PyrUp Example</h2>
+<p>
+    &lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
+    Click <b>Try it</b> button to see the result. You can choose another image.<br>
+    You can change the code in the &lt;textarea&gt; to investigate more.
+</p>
+<div>
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
+</textarea>
+<p class="err" id="errorMessage"></p>
+</div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+    <tr>
+        <td>
+            <canvas id="canvasInput"></canvas>
+        </td>
+        <td>
+            <canvas id="canvasOutput"></canvas>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            <div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
+        </td>
+        <td>
+            <div class="caption">canvasOutput</div>
+        </td>
+    </tr>
+    </table>
+</div>
+<script src="utils.js" type="text/javascript"></script>
+<script id="codeSnippet" type="text/code-snippet">
+let src = cv.imread('canvasInput');
+let dst = new cv.Mat();
+// You can try more different parameters
+cv.pyrUp(src, dst, new cv.Size(0, 0), cv.BORDER_DEFAULT);
+cv.imshow('canvasOutput', dst);
+src.delete(); dst.delete();
+</script>
+<script type="text/javascript">
+let utils = new Utils('errorMessage');
+
+utils.loadCode('codeSnippet', 'codeEditor');
+utils.loadImageToCanvas('lena.jpg', 'canvasInput');
+utils.addFileInputHandler('fileInput', 'canvasInput');
+
+let tryIt = document.getElementById('tryIt');
+tryIt.addEventListener('click', () => {
+    utils.executeCode('codeEditor');
+});
+
+utils.loadOpenCv(() => {
+    tryIt.removeAttribute('disabled');
+});
+</script>
+</body>
+</html>

OpenCV/opencv/doc/js_tutorials/js_assets/js_semantic_segmentation.html

@@ -0,0 +1,243 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Semantic Segmentation Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Semantic Segmentation Example</h2>
+<p>
+    This tutorial shows you how to write an semantic segmentation example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="5" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.The post-processing, including gengerate colors for different classes and argmax to get the classes for each pixel.</p>
+    <textarea class="code" rows="34" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [513, 513];
+mean = [127.5, 127.5, 127.5];
+std = 0.007843;
+swapRB = false;
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const colors = generateColors(result);
+    const output = argmax(result, colors);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet4" type="text/code-snippet">
+generateColors = function(result) {
+    const numClasses = result.matSize[1];
+    let colors = [0,0,0];
+    while(colors.length < numClasses*3){
+        colors.push(Math.round((Math.random()*255 + colors[colors.length-3]) / 2));
+    }
+    return colors;
+}
+
+argmax = function(result, colors) {
+    const C = result.matSize[1];
+    const H = result.matSize[2];
+    const W = result.matSize[3];
+    const resultData = result.data32F;
+    const imgSize = H*W;
+
+    let classId = [];
+    for (i = 0; i<imgSize; ++i) {
+        let id = 0;
+        for (j = 0; j < C; ++j) {
+            if (resultData[j*imgSize+i] > resultData[id*imgSize+i]) {
+                id = j;
+            }
+        }
+        classId.push(colors[id*3]);
+        classId.push(colors[id*3+1]);
+        classId.push(colors[id*3+2]);
+        classId.push(255);
+    }
+
+    output = cv.matFromArray(H,W,cv.CV_8UC4,classId);
+    return output;
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_semantic_segmentation_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor2').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor3').value = loadModelCode;
+
+    utils.loadCode('codeSnippet4', 'codeEditor4');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'roi.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var generateColors = function(result) {};
+    var argmax = function(result, colors) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
+            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
+            cv.imshow('canvasOutput', resized);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>